Main Figures: 1. 000_Figure_2.pdf 2. 000_Figure_3.pdf 3. 000_Figure_4.pdf 4. 000_Figure_5.pdf
Additional Files: 1. 000_Additional_File_4.html 2. 000_Additional_File_5.html 3. 000_Additional_File_6.html 4. 000_Additional_File_7.pdf 5. 000_Additional_File_8.pdf 6. 000_Additional_File_9.html 7. 000_Additional_File_10.html 8. 000_Additional_File_11.html 9. 000_Additional_File_12.html 10. 000_Additional_File_13.html 11. 000_Additional_File_14.html 12. 000_Additional_File_15.pdf 13. 000_Additional_File_16.html 14. 000_Additional_File_17.pdf 15. 000_Additional_File_18.html 16. 000_Additional_File_19.pdf 17. 000_Additional_File_20.html 18. 000_Additional_File_21.pdf 19. 000_Additional_File_22.html 20. 000_Additional_File_23.pdf 21. 000_Additional_File_24.html 22. 000_Additional_File_25.pdf 23. 000_Additional_File_26.html 24. 000_Additional_File_27.pdf
Figure_1, is not output by this script; it is manually created in excel
Table_1 is not created directly from this script, but is built from outputs associated with Additional Files 14:27.
Additional Files 1, 2, and 3 are created by hand in excel and are not created by this script
Additional_File_4 is output with raw p-values, but values are converted to fdr for publication and presented in an xlsx format document. Additional_File_12 is output with raw 2-sided test p-values, but values are converted to 1-sided (higher) and fdr for publication and presented in an xlsx format document.
Prepare the workspace: clean, set options, load packages, load and final formatting on inputs.
rm(list = ls()) # clear workspace
knitr::opts_chunk$set(tidy = TRUE, collapse = TRUE) # set global knitr options.
# load required packages
require(ggplot2); # used for plotting #check#
require(pwr); # used for power analysis
require(lme4); # used for mixed effects models #check#
require(lmerTest); # used to interpret mixed effects models #check#
require(sjPlot); # used to output tables #check#
require(weights); # used for weighted t.test #check#
require(plyr); # used for dataframe manipulation #check#
require(gridExtra) # used for multipanel plotting #check#
require(MuMIn) # model seleciton and multimodel inference
options(scipen = 999) # turn off scientific notation
Load the required datasets and Conduct any last minute calculations
dir_in_pocpro <- "2_CountstoAnalysis/001_Prepare_Datasets" # input directory for Proof of concept processed dataframes
setwd("..")
setwd(dir_in_pocpro) # set to above directory path
load(file = "poc1.Rdata") # load the 1bc control processed dataframe
load(file = "poc2.Rdata") # load the 2 bc control processed dataframe
load(file = "poc92.Rdata") # load the 92 bc proof of concept fitness assays processed dataframe
dir_in_pocraw <- "001_Raw_Data_Metadata" # input directory for Proof of concept raw dataframes
setwd("..")
setwd(dir_in_pocraw) # set to above directory path
load(file = "001_counts.expected.Rdata")
poc.ce <- (counts.expected + 1)[c(1:92), ]
rm(counts.expected) # expected counts
load(file = "001_counts.unexpected.Rdata")
poc.cc <- (counts.unexpected + 1)[c(1:92), ]
rm(counts.unexpected) # cross contamination counts.
dir_in_faevoraw <- "002_Raw_Data_Metadata" # input directory for FA and Evo raw dataframes
setwd("..")
setwd(dir_in_faevoraw) # set to above directory path
load(file = "002_CC_counts.expected.Rdata")
faevo.ce <- (ects + 0)
rm(ects) # expected counts {combined}
load(file = "002_CC_counts.unexpected.Rdata")
faevo.cc <- (ccts + 0)
rm(ccts) # cross contamination counts.{combined}
load(file = "002_counts.expected.Rdata")
faevo.1.ce <- (counts.expected + 1)[c(1:78), ]
rm(counts.expected) # expected counts {first run samples}
load(file = "002_counts.unexpected.Rdata")
faevo.1.cc <- (counts.unexpected + 1)[c(1:78), ]
rm(counts.unexpected) # cross contamination counts. {first run samples}
load(file = "002_Reruns_counts.expected.Rdata")
faevo.2.ce <- (counts.expected + 1)[c(1:78), ]
rm(counts.expected) # expected counts {rerun samples}
load(file = "002_Reruns_counts.unexpected.Rdata")
faevo.2.cc <- (counts.unexpected + 1)[c(1:78), ]
rm(counts.unexpected) # cross contamination counts. {rerun samples}
dir_in_faevopro <- "002_Prepare_Datasets" # input directory for FA and Evo processed dataframes
setwd("..")
setwd(dir_in_faevopro) # set to above directory path
load(file = "myfa.Rdata") # fitness assay
load(file = "myevo.Rdata") # evolution
rm(dir_in_faevopro, dir_in_faevoraw, dir_in_pocpro, dir_in_pocraw)
Create helper functions for linear model residual plotting and data transformation.
# expand_residuals creates a modified histogram of residuals. Each entry in
# the model output is plotted a number of times equal to its reads.
expand_residuals <- function(model, reads) {
myfr <- NA
for (i in 1:length(residuals(model))) {
times <- (((reads[i] - min(reads, na.rm = T))/(max(reads) - min(reads))) *
1000) + 1
myfr <- c(myfr, rep(residuals(model)[i], times = times))
}
return(myfr)
}
# log transformation for modeling.
log_transform <- function(myvar) {
myvar <- log(((myvar - min(myvar))/(max(myvar) - min(myvar))) + 1)
return(myvar)
}
# get legend function found on stack exchange
# ---------------------------------- Source:
# https://stackoverflow.com/questions/12041042/how-to-plot-just-the-legends-in-ggplot2
# scrapes figure legend for later plotting in multipannel plot
get_legend <- function(myggplot) {
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}
Set color palette options for all plots
setpalette <- RColorBrewer::brewer.pal(6, "Dark2") # current preferred palette
# setpalette <-
# c('#993404','#d95f0e','#fe9929','#41b6c4','#2c7fb8','#253494') # option 2
# setpalette <- c('#8c510a','#d8b365','#f6e8c3',
# '#c7eae5','#5ab4ac','#01665e') # option 3
Analysis: Assess POC fitness assay barcode fitness equivalence.
my <- poc92[order(poc92$bcid), ]
# model & diagnostic plots
# ----------------------------------------------------
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r)
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod)
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
hist(residuals(mymod), breaks = 50) # residuals look good, check the other diagnostic plots too...
hist(expand_residuals(mymod, my$r), breaks = 50) # this more accurately represents the distribution of residuals in the model output.
plot(mymod) # view the diagnostic plots
paste0("Number of BCs with fitness different than pop mean = ", sum(p.adjust(coef(summary(mymod))[,
4], method = "fdr") < 0.05)) # number of barcodes with fit different than pop weighted mean.
## [1] "Number of BCs with fitness different than pop mean = 3"
paste0("RsMSE = ", sqrt(mean(residuals(mymod)^2))) # report rmse (requires unscalled lm model)
## [1] "RsMSE = 0.0175768866199638"
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_4.html")
| Â | my$rw corrected | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| d 1 A 10 | 0.00133 | -0.00595 – 0.00861 | 0.35687 | 0.72127977 |
| d 1 A 11 | -0.00762 | -0.01676 – 0.00151 | -1.63579 | 0.10226678 |
| d 1 A 2 | 0.00811 | 0.00035 – 0.01588 | 2.04902 | 0.04077841 |
| d 1 A 3 | 0.00494 | -0.00367 – 0.01356 | 1.12442 | 0.26116464 |
| d 1 A 4 | 0.00796 | -0.00084 – 0.01675 | 1.77364 | 0.07649506 |
| d 1 A 5 | 0.00103 | -0.00916 – 0.01123 | 0.19867 | 0.84257323 |
| d 1 A 6 | -0.00086 | -0.00787 – 0.00616 | -0.23972 | 0.81061012 |
| d 1 A 7 | 0.00344 | -0.00659 – 0.01348 | 0.67247 | 0.50147576 |
| d 1 A 8 | 0.00584 | -0.00229 – 0.01396 | 1.40710 | 0.15977841 |
| d 1 A 9 | 0.00724 | -0.00010 – 0.01459 | 1.93297 | 0.05358384 |
| d 1 B 1 | -0.00527 | -0.01512 – 0.00457 | -1.05048 | 0.29380661 |
| d 1 B 10 | -0.00821 | -0.02028 – 0.00386 | -1.33370 | 0.18267430 |
| d 1 B 11 | -0.01331 | -0.02333 – -0.00328 | -2.60193 | 0.00943744 |
| d 1 B 12 | -0.00742 | -0.01826 – 0.00343 | -1.34063 | 0.18041306 |
| d 1 B 2 | 0.00290 | -0.00556 – 0.01135 | 0.67171 | 0.50195688 |
| d 1 B 3 | 0.00354 | -0.00497 – 0.01206 | 0.81575 | 0.41487762 |
| d 1 B 4 | 0.00951 | 0.00125 – 0.01777 | 2.25665 | 0.02429251 |
| d 1 B 5 | 0.00962 | 0.00135 – 0.01788 | 2.28075 | 0.02281947 |
| d 1 B 6 | 0.00135 | -0.00849 – 0.01118 | 0.26840 | 0.78846186 |
| d 1 B 7 | -0.00398 | -0.01252 – 0.00455 | -0.91533 | 0.36028749 |
| d 1 B 8 | 0.00387 | -0.00467 – 0.01240 | 0.88790 | 0.37485596 |
| d 1 B 9 | -0.00197 | -0.01212 – 0.00817 | -0.38153 | 0.70290904 |
| d 1 C 1 | -0.00130 | -0.01157 – 0.00897 | -0.24854 | 0.80377774 |
| d 1 C 10 | -0.00201 | -0.01402 – 0.01001 | -0.32770 | 0.74322104 |
| d 1 C 11 | 0.00333 | -0.00675 – 0.01340 | 0.64716 | 0.51770702 |
| d 1 C 12 | -0.00496 | -0.01526 – 0.00535 | -0.94240 | 0.34626553 |
| d 1 C 2 | 0.00310 | -0.00554 – 0.01174 | 0.70303 | 0.48223454 |
| d 1 C 3 | 0.00171 | -0.00896 – 0.01237 | 0.31401 | 0.75359137 |
| d 1 C 4 | 0.00064 | -0.00808 – 0.00937 | 0.14452 | 0.88512380 |
| d 1 C 5 | -0.00293 | -0.01216 – 0.00630 | -0.62300 | 0.53345671 |
| d 1 C 6 | -0.00108 | -0.00894 – 0.00678 | -0.27021 | 0.78706863 |
| d 1 C 7 | -0.00224 | -0.01906 – 0.01457 | -0.26145 | 0.79381434 |
| d 1 C 8 | -0.00815 | -0.01866 – 0.00235 | -1.52132 | 0.12856573 |
| d 1 C 9 | 0.01983 | 0.00809 – 0.03157 | 3.31181 | 0.00096754 |
| d 1 D 1 | 0.00371 | -0.00443 – 0.01186 | 0.89388 | 0.37165067 |
| d 1 D 10 | 0.01036 | 0.00241 – 0.01832 | 2.55490 | 0.01080165 |
| d 1 D 11 | -0.00005 | -0.00752 – 0.00742 | -0.01308 | 0.98957011 |
| d 1 D 12 | 0.00302 | -0.00456 – 0.01060 | 0.78029 | 0.43544853 |
| d 1 D 2 | -0.01098 | -0.05482 – 0.03285 | -0.49113 | 0.62346368 |
| d 1 D 3 | -0.00440 | -0.01542 – 0.00662 | -0.78273 | 0.43401108 |
| d 1 D 4 | -0.00694 | -0.02138 – 0.00751 | -0.94144 | 0.34675951 |
| d 1 D 5 | -0.00327 | -0.01432 – 0.00778 | -0.57998 | 0.56208732 |
| d 1 D 6 | -0.00844 | -0.01930 – 0.00242 | -1.52337 | 0.12805285 |
| d 1 D 7 | -0.00168 | -0.01075 – 0.00738 | -0.36387 | 0.71605183 |
| d 1 D 8 | 0.00181 | -0.00598 – 0.00960 | 0.45468 | 0.64946246 |
| d 1 D 9 | 0.00014 | -0.01055 – 0.01084 | 0.02581 | 0.97941897 |
| d 1 E 1 | -0.01271 | -0.02297 – -0.00244 | -2.42696 | 0.01544074 |
| d 1 E 10 | -0.00148 | -0.00997 – 0.00702 | -0.34034 | 0.73369072 |
| d 1 E 11 | -0.00708 | -0.01504 – 0.00088 | -1.74357 | 0.08160859 |
| d 1 E 12 | 0.01157 | 0.00290 – 0.02024 | 2.61533 | 0.00907808 |
| d 1 E 2 | 0.00501 | -0.00231 – 0.01232 | 1.34164 | 0.18008470 |
| d 1 E 3 | 0.00763 | -0.00324 – 0.01849 | 1.37580 | 0.16925925 |
| d 1 E 4 | 0.00592 | -0.00161 – 0.01345 | 1.53993 | 0.12396449 |
| d 1 E 5 | -0.00658 | -0.01635 – 0.00320 | -1.31868 | 0.18764413 |
| d 1 E 6 | -0.00139 | -0.00869 – 0.00591 | -0.37408 | 0.70844388 |
| d 1 E 7 | 0.00852 | 0.00075 – 0.01630 | 2.14783 | 0.03201970 |
| d 1 E 8 | -0.00369 | -0.01238 – 0.00500 | -0.83258 | 0.40532715 |
| d 1 E 9 | 0.00640 | -0.00216 – 0.01495 | 1.46584 | 0.14307587 |
| d 1 F 1 | -0.00012 | -0.00761 – 0.00736 | -0.03272 | 0.97390392 |
| d 1 F 10 | 0.00394 | -0.00436 – 0.01224 | 0.93034 | 0.35246715 |
| d 1 F 11 | 0.00670 | -0.00249 – 0.01590 | 1.42844 | 0.15354746 |
| d 1 F 12 | 0.00778 | 0.00023 – 0.01534 | 2.01954 | 0.04375588 |
| d 1 F 2 | 0.00583 | -0.00270 – 0.01436 | 1.33950 | 0.18078013 |
| d 1 F 3 | 0.00651 | -0.00518 – 0.01820 | 1.09171 | 0.27528055 |
| d 1 F 4 | -0.02625 | -0.04436 – -0.00815 | -2.84193 | 0.00459550 |
| d 1 F 5 | -0.04834 | -0.05677 – -0.03992 | -11.24575 | <0.001 |
| d 1 F 6 | -0.00013 | -0.00761 – 0.00735 | -0.03389 | 0.97297191 |
| d 1 F 7 | -0.01483 | -0.02450 – -0.00517 | -3.00919 | 0.00269975 |
| d 1 F 8 | -0.00220 | -0.01408 – 0.00967 | -0.36333 | 0.71645463 |
| d 1 F 9 | 0.00030 | -0.02257 – 0.02316 | 0.02551 | 0.97965060 |
| d 1 G 1 | 0.01007 | -0.00276 – 0.02290 | 1.53824 | 0.12437600 |
| d 1 G 10 | 0.00690 | -0.00012 – 0.01393 | 1.92634 | 0.05440800 |
| d 1 G 11 | 0.00299 | -0.00793 – 0.01391 | 0.53730 | 0.59120517 |
| d 1 G 12 | 0.00147 | -0.00528 – 0.00823 | 0.42786 | 0.66886664 |
| d 1 G 2 | 0.00616 | -0.00501 – 0.01732 | 1.08082 | 0.28009589 |
| d 1 G 3 | 0.00098 | -0.00668 – 0.00864 | 0.24981 | 0.80279516 |
| d 1 G 4 | -0.00336 | -0.01274 – 0.00603 | -0.70111 | 0.48343252 |
| d 1 G 5 | -0.01510 | -0.02563 – -0.00458 | -2.81189 | 0.00504284 |
| d 1 G 6 | -0.00650 | -0.01578 – 0.00277 | -1.37514 | 0.16946284 |
| d 1 G 7 | -0.00912 | -0.01756 – -0.00068 | -2.11732 | 0.03453391 |
| d 1 G 8 | -0.00467 | -0.01426 – 0.00492 | -0.95479 | 0.33996652 |
| d 1 G 9 | -0.00346 | -0.01268 – 0.00576 | -0.73514 | 0.46246280 |
| d 1 H 11 | 0.00189 | -0.00572 – 0.00949 | 0.48611 | 0.62701817 |
| d 1 H 2 | -0.00688 | -0.01738 – 0.00362 | -1.28489 | 0.19919285 |
| d 1 H 3 | -0.00075 | -0.01331 – 0.01181 | -0.11669 | 0.90713761 |
| d 1 H 4 | -0.03017 | -0.04830 – -0.01204 | -3.26136 | 0.00115463 |
| d 1 H 5 | -0.00289 | -0.01148 – 0.00570 | -0.66010 | 0.50937740 |
| d 1 H 6 | 0.00117 | -0.00599 – 0.00833 | 0.32057 | 0.74862140 |
| d 1 H 7 | -0.00459 | -0.02131 – 0.01212 | -0.53860 | 0.59031160 |
| d 1 H 8 | -0.00450 | -0.01653 – 0.00754 | -0.73220 | 0.46425473 |
| d 1 H 9 | -0.00799 | -0.01945 – 0.00347 | -1.36586 | 0.17235858 |
| Observations | 910 | |||
| R2 / R2 adjusted | 0.265 / 0.184 | |||
# -----------------------------------------------------------------------------
# create temporary plotting dataframe
# -----------------------------------------
myres <- as.data.frame(coef(summary(mymod)), stringsAsFactors = F) # create a myres dataframe to aid in plotting.
myres$p.adjust <- p.adjust(myres$`Pr(>|t|)`, method = "fdr") # adjusted p-values
myres$bcid <- gsub("^.*?d", "", rownames(myres)) # fix barcode id names
myres$mrw <- ddply(my, ~bcid, function(my) weighted.mean(my$rw, my$r))[, 2] # weighted mean for fitness
myres$rmrw <- ddply(my, ~bcid, function(my) mean(my$r))[, 2] # mean for weights
myres$mcmrw <- myres$mrw - weighted.mean(myres$mrw, myres$rmrw) # mean corrected fitness
myres$color <- "fdr > 0.05"
myres$color[myres$p.adjust <= 0.05] <- "fdr <= 0.05" # set plotting colors
myres$color <- as.factor(myres$color)
myres$color <- relevel(myres$color, "fdr > 0.05")
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- myres$mcmrw
myr <- myres$rmrw
myc <- myres$color
mybinwidth <- 0.0025
mytitle <- NULL
myylab <- "Count"
myxlab <- "Fitness difference from population mean"
myhighlight <- "black"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.345
mywidth <- 3.345
p <- ggplot(myres, aes(x = myx, color = myc, fill = myc)) + geom_histogram(alpha = myfillapha,
position = "identity", binwidth = mybinwidth) + geom_rug(alpha = myptalpha) +
# xlim(c(-0.06, 0.06)) +
ylim(c(NA, 15)) + geom_vline(xintercept = 0, linetype = "dashed", color = myhighlight) +
scale_color_manual(values = mypalette, guide = FALSE) + scale_fill_manual(values = mypalette) +
theme_classic() + theme(legend.position = c(0.8, 0.8)) + labs(fill = "Significance") +
ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) + geom_hline(yintercept = -0.01,
colour = "white", size = 1.01) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
theme(legend.title = element_text(size = mytextsize, face = "bold")) + theme(legend.text = element_text(size = mytextsize)) +
scale_x_continuous(breaks = seq(-0.06, 0.06, by = 0.01), labels = c("-0.06",
"", "-0.04", "", "-0.02", "", "0", "", "0.02", "", "0.04", "", "0.06"),
limits = c(-0.06, 0.06))
p
## Warning: Removed 4 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Figure_2.pdf", width = mywidth, height = myheight)
p
## Warning: Removed 4 rows containing missing values (geom_bar).
dev.off() # one column wide, equal height
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(p, myres, mymod, my, mybinwidth, myc, myfillapha, myheight, myhighlight,
mylinealpha, myptalpha, myr, mytextsize, mytitle, mywidth, myx, myxlab,
myylab, mypalette)
Analysis: Calculate sequenced library reads summary values
paste0("matching counts (match f and r primer barcode, and moby bc): ", sum(c(poc.ce,
poc.cc, faevo.1.ce, faevo.1.cc, faevo.2.ce, faevo.2.cc), na.rm = T)) # match forward primer, reverse primer, and yeast strain BCs
## [1] "matching counts (match f and r primer barcode, and moby bc): 104365740"
paste0("analysis counts: ", sum(c(poc.ce, poc.cc, faevo.ce, faevo.cc), na.rm = T)) # expected and cc counts in the analysis data
## [1] "analysis counts: 84776627"
paste0("expected analysis counts: ", sum(c(poc.ce, faevo.ce), na.rm = T)) # expected counts in the analysis data
## [1] "expected analysis counts: 81780343"
paste0("median counts per barcode: ", median(c(poc.ce, faevo.ce), na.rm = T)) # median number of counts per barcode in the expected counts dataset.
## [1] "median counts per barcode: 2961"
paste0("median counts per barcode: ", median(c(faevo.ce), na.rm = T)) # median number of counts per barcode in the expected counts dataset.
## [1] "median counts per barcode: 2913.5"
paste0("median counts per barcode: ", median(c(poc.ce), na.rm = T)) # median number of counts per barcode in the expected counts dataset.
## [1] "median counts per barcode: 3070"
my <- c(faevo.ce, poc.ce)
median(my, na.rm = T)
## [1] 2961
Analysis: Assess POC library barcode cross-contamination rate.
# calculations
# ----------------------------------------------------------------
cc = (((colSums(poc.cc, na.rm = T))/(colSums(poc.ce, na.rm = T) + colSums(poc.cc,
na.rm = T)))/colSums(!is.na(poc.cc))) * 100 # cc rate per exp
cc[is.nan(cc)] <- NA # NA nans
myt <- as.data.frame(cc) # convert to dataframe
myt$tc <- (colSums(poc.ce, na.rm = T) + colSums(poc.cc, na.rm = T)) # total counts to use as weight in weighted mean
cc001 <- weighted.mean(myt$cc, myt$tc, na.rm = T) # save weighted mean cc (by expected reads) for grand cc rate.
r001 <- mean(myt$tc) # save mean for below calculation of grand cc rate.
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- myt$cc
myr <- myt$tc
myc <- setpalette[1]
mybinwidth <- 0.01
mytitle <- NULL
myylab <- "Count"
myxlab <- "Mean Barcode Contamination Rate (Percent Total Counts)"
myhighlight <- "black"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
p_a <- ggplot(myt, aes(x = myx)) + geom_histogram(alpha = myfillapha, color = myc,
fill = myc, binwidth = mybinwidth) + geom_vline(xintercept = weighted.mean(myx,
myr, na.rm = T), linetype = "dashed", color = myhighlight) + geom_rug(color = myc,
alpha = myptalpha) + xlim(NA, 1) + theme_classic() + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
geom_hline(yintercept = -0.01, colour = "white", size = 1.01) + annotate("text",
x = 1, y = 47, label = "A", size = 10)
p_a
## Warning: Removed 20 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
rm(cc, myt) # remove the temporary variable
Analysis: Assess 250-generation experiment consensus library barcode cross-contamination rate.
# Calculations
# ----------------------------------------------------------------
cc = ((colSums(faevo.cc, na.rm = T))/(colSums(faevo.ce, na.rm = T) + colSums(faevo.cc,
na.rm = T)))/colSums(!is.na(faevo.cc)) * 100 # cc rate per exp
cc[is.nan(cc)] <- NA # NA nans
myt <- as.data.frame(cc) # convert to dataframe
myt$tc <- (colSums(faevo.ce, na.rm = T) + colSums(faevo.cc, na.rm = T)) # total counts to use as weight in weighted mean
cc002 <- weighted.mean(myt$cc, myt$tc, na.rm = T) # save weighted mean cc (by expected reads) for grand cc rate.
r002 <- mean(myt$tc) # save mean for below calculation of grand cc rate.
# overall cc rate
paste0("mean cross contam as % total counts = ", weighted.mean(c(cc001, cc002),
c(r001, r002)), " %") # weighted mean contam weighted.mean(c(contam rate poc, contam rate faevo), c(total counts poc, total counts faevo))
## [1] "mean cross contam as % total counts = 0.0427158666120247 %"
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx2 <- myt$cc
myr <- myt$tc
myc <- setpalette[1]
mybinwidth <- 0.01
mytitle <- NULL
myylab <- "Count"
myxlab <- "Mean Barcode Contamination Rate (Percent Total Counts)"
myhighlight <- "black"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mytextsize <- 8
myheight <- 7
mywidth <- 7
p_b <- ggplot(myt, aes(x = myx2)) + geom_histogram(alpha = myfillapha, color = myc,
fill = myc, binwidth = mybinwidth) + geom_vline(xintercept = weighted.mean(myx2,
myr, na.rm = T), linetype = "dashed", color = myhighlight) + geom_rug(color = myc,
alpha = myptalpha) + xlim(NA, 1) + theme_classic() + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
geom_hline(yintercept = -0.01, colour = "white", size = 1.01) + annotate("text",
x = 1, y = 50, label = "B", size = 10)
p_b
## Warning: Removed 1 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
grid.arrange(p_a, p_b, nrow = 2)
## Warning: Removed 20 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_bar).
pdf(file = "000_Additional_File_7.pdf", width = mywidth, height = myheight)
grid.arrange(p_a, p_b, nrow = 2)
## Warning: Removed 20 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 1 rows containing missing values (geom_bar).
dev.off() # 2columns wide, half as tall
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(cc, myt, p_a, p_b, cc001, cc002, r001, r002, myx2, mybinwidth, myc, myfillapha,
myheight, myhighlight, mylinealpha, myptalpha, myr, mytextsize, mytitle,
mywidth, myx, myxlab, myylab) # remove the temporary variable
Analysis: Assess change in barcode cross-contamination rate for samples from the 250-generation experiment that were resequenced.
# Calculations & models
# -------------------------------------------------------
faevo.1.ce <- faevo.1.ce[, (colnames(faevo.1.ce) %in% colnames(faevo.2.ce))] # subset the run 1 data to only include those columns in the rerun dataset..expected counts..
faevo.1.cc <- faevo.1.cc[, (colnames(faevo.1.cc) %in% colnames(faevo.2.cc))] # cross contamination counts
faevo.1.ce <- faevo.1.ce[, colnames(faevo.1.ce)]
faevo.1.cc <- faevo.1.cc[, colnames(faevo.1.ce)] # reorder the run 1 data by column name
faevo.2.ce <- faevo.2.ce[, colnames(faevo.1.ce)]
faevo.2.cc <- faevo.2.cc[, colnames(faevo.1.ce)] # reorder the run 2 data by run 1 column name (for paired t test below)
myt1 <- ((colSums(faevo.1.cc, na.rm = T))/(colSums(faevo.1.ce, na.rm = T) +
colSums(faevo.1.cc, na.rm = T)))/colSums(!is.na(faevo.1.cc)) # cross contam run 1
myt2 <- ((colSums(faevo.2.cc, na.rm = T))/(colSums(faevo.2.ce, na.rm = T) +
colSums(faevo.2.cc, na.rm = T)))/colSums(!is.na(faevo.2.cc)) # cross contam run 2
myt1r <- (colSums(faevo.1.ce, na.rm = T) + colSums(faevo.1.cc, na.rm = T)) # reads run 1
myt2r <- (colSums(faevo.2.ce, na.rm = T) + colSums(faevo.2.cc, na.rm = T)) # reads run 2
myt <- myt1 - myt2 # calculatation for weighted t.test below (contam run 1 - contam run 2)
mytr <- 2/((1/myt1r) + (1/myt2r)) # reads for calculation below (harmonic mean of reads for run 1 and run 2)
mymod2 <- wtd.t.test(x = myt, y = 0, weight = mytr, alternative = "greater",
mean1 = T) # t.test, paired, 1 sided (greater), weighted
# -----------------------------------------------------------------------------
# create temporary plotting dataframe
# -----------------------------------------
myt1 <- as.data.frame(cbind(myt1, myt1r))
colnames(myt1) <- c("cc", "r")
myt1$seq.run <- 1 # run 1 df
myt2 <- as.data.frame(cbind(myt2, myt2r))
colnames(myt2) <- c("cc", "r")
myt2$seq.run <- 2 # run 2 df
myt <- rbind(myt1, myt2)
myt$seq.run <- as.factor(myt$seq.run) # combine
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- myt$seq.run
myy <- myt$cc
myr <- myt$r
myx1 <- myt1$cc
myr1 <- myt1$r
myx2 <- myt2$cc
myr2 <- myt2$r
mytitle <- NULL
myylab <- "Mean Barcode Contamination Rate (% Total Counts)"
myxlab <- "Sequenced Libary Run Number"
myoutline <- "grey50"
myfill <- "grey90"
myhighlight <- "grey50"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 7
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(myt, aes(x = myx, y = myy, weight = myr, color = myx)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_segment(x = 0.75, xend = 1.25, y = weighted.mean(myx1,
myr1), yend = weighted.mean(myx1, myr1), color = myhighlight) + geom_segment(x = 1.75,
xend = 2.25, y = weighted.mean(myx2, myr2), yend = weighted.mean(myx2, myr2),
color = myhighlight) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha,
size = myptsize) + geom_rug(sides = "l", alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + theme_classic() + theme(legend.position = "none") +
ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) + scale_x_discrete(breaks = c("1",
"2"), labels = c("Library 1", "Library 2")) + theme(axis.title.y = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0))
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_8.pdf", width = mywidth, height = myheight)
p
dev.off() # 7by7
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(faevo.1.ce, faevo.1.cc, faevo.2.ce, faevo.2.cc, myt1, myt1r, myt2, myt2r,
myt, mytr, p) # clean up this section.
rm(faevo.cc, faevo.ce, poc.cc, poc.ce) # clean up for next major set of analyses (remove the counts datasets to keep our global environment clean)
rm(myfill, myfillapha, myheight, myhighlight, mylinealpha, myoutline, mypalette,
myptalpha, myptsize, myr, myr1, myr2, mytextsize, mytitle, mywidth, myx,
myx1, myx2, myxlab, myy, myylab) # remove plotting params
Analysis: Assess fitness change in 250-generations of experimental evolution for barcodes in the 250-generation experiment.
my <- myfa[order(myfa$bctID), ]
# model & diagnostic plots
# ----------------------------------------------------
mymod <- lm(deltafit ~ bctID + 0, weights = rdeltafit, data = my)
summary(mymod)
##
## Call:
## lm(formula = deltafit ~ bctID + 0, data = my, weights = rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## bctIDCM+Saltdiploid0.001d1A3 0.153770
## bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## bctIDCM+Saltdiploid0.001d1F5 0.208173
## bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## bctIDCM+Saltdiploid0.001d1H2 0.747562
## bctIDCM+Saltdiploid0.001d1H3 0.191568
## bctIDCM+Saltdiploid0.001d1H4 0.269265
## bctIDCM+Saltdiploid0.001d1H5 0.767557
## bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## bctIDCMdiploid0.00025d1A2 0.329882
## bctIDCMdiploid0.00025d1A3 0.557181
## bctIDCMdiploid0.00025d1A4 0.903664
## bctIDCMdiploid0.00025d1A5 0.077226 .
## bctIDCMdiploid0.00025d1A6 0.358086
## bctIDCMdiploid0.00025d1A7 0.786112
## bctIDCMdiploid0.00025d1B10 0.624463
## bctIDCMdiploid0.00025d1B11 0.166007
## bctIDCMdiploid0.00025d1B12 0.103764
## bctIDCMdiploid0.00025d1B9 0.107440
## bctIDCMdiploid0.00025d1C10 0.368373
## bctIDCMdiploid0.00025d1C11 0.048000 *
## bctIDCMdiploid0.00025d1C12 0.259774
## bctIDCMdiploid0.00025d1C9 0.662997
## bctIDCMdiploid0.00025d1D2 0.946757
## bctIDCMdiploid0.00025d1E2 0.221035
## bctIDCMdiploid0.00025d1H2 0.666246
## bctIDCMdiploid0.00025d1H3 0.792714
## bctIDCMdiploid0.00025d1H4 0.447657
## bctIDCMdiploid0.00025d1H5 0.065659 .
## bctIDCMdiploid0.00025d1H6 0.759244
## bctIDCMdiploid0.00025d1H7 0.945372
## bctIDCMdiploid0.001d1A11 0.047218 *
## bctIDCMdiploid0.001d1A2 0.568387
## bctIDCMdiploid0.001d1A2B 0.445453
## bctIDCMdiploid0.001d1A3 0.982727
## bctIDCMdiploid0.001d1A3B 0.774047
## bctIDCMdiploid0.001d1A4 0.174949
## bctIDCMdiploid0.001d1A4B 0.908856
## bctIDCMdiploid0.001d1A5 0.061817 .
## bctIDCMdiploid0.001d1A5B 0.726909
## bctIDCMdiploid0.001d1A6 0.074694 .
## bctIDCMdiploid0.001d1A6B 0.476265
## bctIDCMdiploid0.001d1A7 0.001518 **
## bctIDCMdiploid0.001d1A8 0.196716
## bctIDCMdiploid0.001d1A9 0.044951 *
## bctIDCMdiploid0.001d1B1 0.006576 **
## bctIDCMdiploid0.001d1B2 0.003039 **
## bctIDCMdiploid0.001d1C1 0.002576 **
## bctIDCMdiploid0.001d1C2 0.013632 *
## bctIDCMdiploid0.001d1D4 0.096546 .
## bctIDCMdiploid0.001d1D5 0.152415
## bctIDCMdiploid0.001d1D6 0.315226
## bctIDCMdiploid0.001d1D7 0.310496
## bctIDCMdiploid0.001d1D8 0.240516
## bctIDCMdiploid0.001d1E4 0.700111
## bctIDCMdiploid0.001d1E5 0.197395
## bctIDCMdiploid0.001d1E6 0.023170 *
## bctIDCMdiploid0.001d1E7 0.008807 **
## bctIDCMdiploid0.001d1E8 0.690775
## bctIDCMdiploid0.001d1H11 0.038086 *
## bctIDCMdiploid0.001d1H2 0.502150
## bctIDCMdiploid0.001d1H2B 0.304231
## bctIDCMdiploid0.001d1H3 0.767194
## bctIDCMdiploid0.001d1H3B 0.998072
## bctIDCMdiploid0.001d1H4 0.721741
## bctIDCMdiploid0.001d1H4B 0.915822
## bctIDCMdiploid0.001d1H5 0.374428
## bctIDCMdiploid0.001d1H5B 0.839864
## bctIDCMdiploid0.001d1H6 0.011740 *
## bctIDCMdiploid0.001d1H6B 0.055013 .
## bctIDCMdiploid0.001d1H7 0.722004
## bctIDCMdiploid0.001d1H8 0.534803
## bctIDCMdiploid0.001d1H9 0.407275
## bctIDCMdiploid0.004d1A2 0.965323
## bctIDCMdiploid0.004d1A3 0.522127
## bctIDCMdiploid0.004d1A4 0.003677 **
## bctIDCMdiploid0.004d1A5 0.004378 **
## bctIDCMdiploid0.004d1A6 0.262755
## bctIDCMdiploid0.004d1A7 0.153420
## bctIDCMdiploid0.004d1B3 0.089106 .
## bctIDCMdiploid0.004d1B4 0.060315 .
## bctIDCMdiploid0.004d1B5 0.001775 **
## bctIDCMdiploid0.004d1B6 0.002307 **
## bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## bctIDCMdiploid0.004d1C3 0.003811 **
## bctIDCMdiploid0.004d1C4 0.096605 .
## bctIDCMdiploid0.004d1C5 0.000868 ***
## bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## bctIDCMdiploid0.004d1C7 0.186138
## bctIDCMdiploid0.004d1H2 0.335547
## bctIDCMdiploid0.004d1H3 0.333005
## bctIDCMdiploid0.004d1H4 0.857174
## bctIDCMdiploid0.004d1H5 0.001720 **
## bctIDCMdiploid0.004d1H6 0.023234 *
## bctIDCMdiploid0.004d1H7 0.712242
## bctIDCMhaploid0.001d1A2 0.337921
## bctIDCMhaploid0.001d1A3 0.034829 *
## bctIDCMhaploid0.001d1A3B 0.020091 *
## bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## bctIDCMhaploid0.001d1A4B 0.504764
## bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## bctIDCMhaploid0.001d1A6 0.005879 **
## bctIDCMhaploid0.001d1A6B 0.002633 **
## bctIDCMhaploid0.001d1A7 0.216581
## bctIDCMhaploid0.001d1A7B 0.177968
## bctIDCMhaploid0.001d1H2 0.000139 ***
## bctIDCMhaploid0.001d1H3 0.726659
## bctIDCMhaploid0.001d1H3B 0.219285
## bctIDCMhaploid0.001d1H4 0.090830 .
## bctIDCMhaploid0.001d1H4B 0.465230
## bctIDCMhaploid0.001d1H5 0.236513
## bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## bctIDCMhaploid0.001d1H6 0.000766 ***
## bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
hist(residuals(mymod), breaks = 100) # tails do look a little heavy
hist(expand_residuals(mymod, my$rdeltafit), breaks = 100) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # model fit (multi) -- looks pretty good.
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_12.html") # table does not have corrected p-values.
| Â | deltafit | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| CM+Ethanoldiploid 0.001 d 1 A 2 | 0.00225 | -0.02174 – 0.02623 | 0.18359 | 0.85441888 |
| CM+Ethanoldiploid 0.001 d 1 A 4 | 0.01765 | -0.00912 – 0.04443 | 1.29252 | 0.19683180 |
| CM+Ethanoldiploid 0.001 d 1 A 5 | -0.00183 | -0.03321 – 0.02955 | -0.11438 | 0.90898955 |
| CM+Ethanoldiploid 0.001 d 1 A 6 | 0.00848 | -0.01426 – 0.03122 | 0.73068 | 0.46535168 |
| CM+Ethanoldiploid 0.001 d 1 A 7 | 0.00837 | -0.02254 – 0.03929 | 0.53067 | 0.59590266 |
| CM+Ethanoldiploid 0.001 d 1 D 10 | 0.03355 | 0.01288 – 0.05422 | 3.18066 | 0.00156959 |
| CM+Ethanoldiploid 0.001 d 1 D 11 | 0.02981 | 0.00838 – 0.05124 | 2.72640 | 0.00664970 |
| CM+Ethanoldiploid 0.001 d 1 D 12 | 0.02399 | 0.00403 – 0.04395 | 2.35579 | 0.01890587 |
| CM+Ethanoldiploid 0.001 d 1 D 9 | 0.02343 | -0.01630 – 0.06316 | 1.15600 | 0.24828733 |
| CM+Ethanoldiploid 0.001 d 1 E 10 | 0.04808 | 0.02612 – 0.07003 | 4.29187 | 0.00002165 |
| CM+Ethanoldiploid 0.001 d 1 E 11 | 0.03162 | 0.00956 – 0.05369 | 2.80906 | 0.00518210 |
| CM+Ethanoldiploid 0.001 d 1 E 12 | -0.03049 | -0.05423 – -0.00676 | -2.51790 | 0.01214729 |
| CM+Ethanoldiploid 0.001 d 1 E 9 | -0.01000 | -0.03098 – 0.01099 | -0.93380 | 0.35090332 |
| CM+Ethanoldiploid 0.001 d 1 F 1 | 0.00939 | -0.00784 – 0.02661 | 1.06769 | 0.28622445 |
| CM+Ethanoldiploid 0.001 d 1 F 2 | 0.01938 | -0.00260 – 0.04137 | 1.72835 | 0.08460206 |
| CM+Ethanoldiploid 0.001 d 1 G 1 | 0.01658 | -0.01283 – 0.04598 | 1.10473 | 0.26985901 |
| CM+Ethanoldiploid 0.001 d 1 G 2 | -0.01128 | -0.04692 – 0.02437 | -0.62003 | 0.53554478 |
| CM+Ethanoldiploid 0.001 d 1 H 2 | -0.01386 | -0.04987 – 0.02215 | -0.75441 | 0.45099161 |
| CM+Ethanoldiploid 0.001 d 1 H 4 | 0.00296 | -0.04362 – 0.04955 | 0.12470 | 0.90081956 |
| CM+Ethanoldiploid 0.001 d 1 H 5 | 0.00347 | -0.02360 – 0.03055 | 0.25150 | 0.80154341 |
| CM+Ethanoldiploid 0.001 d 1 H 6 | 0.00334 | -0.02173 – 0.02841 | 0.26095 | 0.79424982 |
| CM+Ethanoldiploid 0.001 d 1 H 7 | -0.00552 | -0.05566 – 0.04462 | -0.21577 | 0.82926550 |
| CM+Saltdiploid 0.001 d 1 A 2 | 0.04404 | 0.02040 – 0.06767 | 3.65171 | 0.00029070 |
| CM+Saltdiploid 0.001 d 1 A 3 | 0.01687 | -0.00627 – 0.04001 | 1.42872 | 0.15376998 |
| CM+Saltdiploid 0.001 d 1 A 4 | 0.02820 | 0.00390 – 0.05251 | 2.27428 | 0.02341305 |
| CM+Saltdiploid 0.001 d 1 A 5 | 0.39853 | 0.01306 – 0.78400 | 2.02637 | 0.04330880 |
| CM+Saltdiploid 0.001 d 1 A 7 | 0.02579 | -0.00014 – 0.05172 | 1.94921 | 0.05188310 |
| CM+Saltdiploid 0.001 d 1 F 3 | 0.14080 | 0.10093 – 0.18066 | 6.92234 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 4 | 0.18806 | 0.14305 – 0.23307 | 8.18925 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 5 | 0.19914 | -0.11053 – 0.50881 | 1.26039 | 0.20817273 |
| CM+Saltdiploid 0.001 d 1 F 6 | 0.23451 | 0.20549 – 0.26353 | 15.83705 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 7 | 0.15255 | 0.11098 – 0.19412 | 7.19216 | <0.001 |
| CM+Saltdiploid 0.001 d 1 F 8 | 0.12889 | 0.09437 – 0.16340 | 7.31927 | <0.001 |
| CM+Saltdiploid 0.001 d 1 G 3 | 0.10101 | 0.02422 – 0.17781 | 2.57810 | 0.01024726 |
| CM+Saltdiploid 0.001 d 1 G 4 | 0.13403 | 0.04549 – 0.22256 | 2.96698 | 0.00316552 |
| CM+Saltdiploid 0.001 d 1 G 5 | 0.09585 | 0.05790 – 0.13381 | 4.95013 | 0.00000105 |
| CM+Saltdiploid 0.001 d 1 G 6 | 0.08971 | 0.02532 – 0.15411 | 2.73063 | 0.00656636 |
| CM+Saltdiploid 0.001 d 1 G 7 | 0.08740 | 0.04612 – 0.12868 | 4.14957 | 0.00003975 |
| CM+Saltdiploid 0.001 d 1 G 8 | 0.12572 | 0.06124 – 0.19021 | 3.82139 | 0.00015111 |
| CM+Saltdiploid 0.001 d 1 H 2 | 0.00496 | -0.02522 – 0.03513 | 0.32205 | 0.74756239 |
| CM+Saltdiploid 0.001 d 1 H 3 | 0.02116 | -0.01055 – 0.05286 | 1.30790 | 0.19156762 |
| CM+Saltdiploid 0.001 d 1 H 4 | 0.02137 | -0.01650 – 0.05925 | 1.10610 | 0.26926505 |
| CM+Saltdiploid 0.001 d 1 H 5 | 0.00355 | -0.01997 – 0.02707 | 0.29575 | 0.76755679 |
| CM+Saltdiploid 0.001 d 1 H 7 | 0.09418 | 0.04275 – 0.14561 | 3.58899 | 0.00036794 |
| C Mdiploid 0.00025 d 1 A 2 | 0.01560 | -0.01575 – 0.04695 | 0.97539 | 0.32988206 |
| C Mdiploid 0.00025 d 1 A 3 | -0.00879 | -0.03810 – 0.02053 | -0.58747 | 0.55718131 |
| C Mdiploid 0.00025 d 1 A 4 | 0.00175 | -0.02651 – 0.03001 | 0.12110 | 0.90366385 |
| C Mdiploid 0.00025 d 1 A 5 | 0.02678 | -0.00286 – 0.05641 | 1.77102 | 0.07722576 |
| C Mdiploid 0.00025 d 1 A 6 | 0.01199 | -0.01355 – 0.03753 | 0.91995 | 0.35808612 |
| C Mdiploid 0.00025 d 1 A 7 | -0.00440 | -0.03618 – 0.02738 | -0.27152 | 0.78611237 |
| C Mdiploid 0.00025 d 1 B 10 | -0.00810 | -0.04050 – 0.02431 | -0.48987 | 0.62446295 |
| C Mdiploid 0.00025 d 1 B 11 | 0.01723 | -0.00711 – 0.04158 | 1.38737 | 0.16600725 |
| C Mdiploid 0.00025 d 1 B 12 | 0.02984 | -0.00604 – 0.06571 | 1.63013 | 0.10376368 |
| C Mdiploid 0.00025 d 1 B 9 | 0.02155 | -0.00464 – 0.04774 | 1.61298 | 0.10744003 |
| C Mdiploid 0.00025 d 1 C 10 | -0.01674 | -0.05318 – 0.01970 | -0.90042 | 0.36837253 |
| C Mdiploid 0.00025 d 1 C 11 | 0.03217 | 0.00037 – 0.06396 | 1.98271 | 0.04799999 |
| C Mdiploid 0.00025 d 1 C 12 | 0.01497 | -0.01103 – 0.04097 | 1.12833 | 0.25977419 |
| C Mdiploid 0.00025 d 1 C 9 | -0.00518 | -0.02846 – 0.01810 | -0.43606 | 0.66299683 |
| C Mdiploid 0.00025 d 1 D 2 | 0.00371 | -0.10509 – 0.11251 | 0.06682 | 0.94675682 |
| C Mdiploid 0.00025 d 1 E 2 | 0.01289 | -0.00773 – 0.03352 | 1.22546 | 0.22103521 |
| C Mdiploid 0.00025 d 1 H 2 | 0.00761 | -0.02693 – 0.04215 | 0.43159 | 0.66624601 |
| C Mdiploid 0.00025 d 1 H 3 | -0.00459 | -0.03880 – 0.02962 | -0.26294 | 0.79271446 |
| C Mdiploid 0.00025 d 1 H 4 | 0.01844 | -0.02911 – 0.06599 | 0.75998 | 0.44765688 |
| C Mdiploid 0.00025 d 1 H 5 | 0.02542 | -0.00158 – 0.05242 | 1.84519 | 0.06565851 |
| C Mdiploid 0.00025 d 1 H 6 | 0.00415 | -0.02236 – 0.03065 | 0.30666 | 0.75924362 |
| C Mdiploid 0.00025 d 1 H 7 | 0.00191 | -0.05263 – 0.05644 | 0.06856 | 0.94537182 |
| C Mdiploid 0.001 d 1 A 11 | 0.03612 | 0.00054 – 0.07170 | 1.98973 | 0.04721759 |
| C Mdiploid 0.001 d 1 A 2 | 0.00809 | -0.01969 – 0.03588 | 0.57084 | 0.56838732 |
| C Mdiploid 0.001 d 1 A 2 B | 0.00927 | -0.01453 – 0.03307 | 0.76368 | 0.44545327 |
| C Mdiploid 0.001 d 1 A 3 | 0.00032 | -0.02879 – 0.02943 | 0.02166 | 0.98272673 |
| C Mdiploid 0.001 d 1 A 3 B | 0.00398 | -0.02318 – 0.03114 | 0.28726 | 0.77404701 |
| C Mdiploid 0.001 d 1 A 4 | 0.01781 | -0.00788 – 0.04350 | 1.35859 | 0.17494851 |
| C Mdiploid 0.001 d 1 A 4 B | 0.00153 | -0.02470 – 0.02777 | 0.11455 | 0.90885560 |
| C Mdiploid 0.001 d 1 A 5 | 0.02938 | -0.00138 – 0.06013 | 1.87221 | 0.06181742 |
| C Mdiploid 0.001 d 1 A 5 B | -0.00512 | -0.03385 – 0.02361 | -0.34945 | 0.72690929 |
| C Mdiploid 0.001 d 1 A 6 | 0.02327 | -0.00226 – 0.04881 | 1.78643 | 0.07469382 |
| C Mdiploid 0.001 d 1 A 6 B | 0.00955 | -0.01671 – 0.03581 | 0.71291 | 0.47626537 |
| C Mdiploid 0.001 d 1 A 7 | 0.04291 | 0.01655 – 0.06926 | 3.19053 | 0.00151809 |
| C Mdiploid 0.001 d 1 A 8 | 0.01513 | -0.00780 – 0.03806 | 1.29286 | 0.19671558 |
| C Mdiploid 0.001 d 1 A 9 | 0.02268 | 0.00057 – 0.04479 | 2.01065 | 0.04495102 |
| C Mdiploid 0.001 d 1 B 1 | 0.05233 | 0.01476 – 0.08989 | 2.73013 | 0.00657598 |
| C Mdiploid 0.001 d 1 B 2 | 0.04063 | 0.01391 – 0.06736 | 2.97975 | 0.00303894 |
| C Mdiploid 0.001 d 1 C 1 | 0.05497 | 0.01942 – 0.09051 | 3.03106 | 0.00257568 |
| C Mdiploid 0.001 d 1 C 2 | 0.03217 | 0.00671 – 0.05764 | 2.47643 | 0.01363235 |
| C Mdiploid 0.001 d 1 D 4 | 0.08814 | -0.01560 – 0.19188 | 1.66527 | 0.09654594 |
| C Mdiploid 0.001 d 1 D 5 | -0.01879 | -0.04448 – 0.00690 | -1.43345 | 0.15241540 |
| C Mdiploid 0.001 d 1 D 6 | 0.04266 | -0.04051 – 0.12583 | 1.00542 | 0.31522648 |
| C Mdiploid 0.001 d 1 D 7 | 0.01221 | -0.01136 – 0.03578 | 1.01531 | 0.31049575 |
| C Mdiploid 0.001 d 1 D 8 | -0.01271 | -0.03390 – 0.00849 | -1.17523 | 0.24051605 |
| C Mdiploid 0.001 d 1 E 4 | 0.00302 | -0.01233 – 0.01836 | 0.38541 | 0.70011148 |
| C Mdiploid 0.001 d 1 E 5 | 0.03661 | -0.01898 – 0.09221 | 1.29089 | 0.19739471 |
| C Mdiploid 0.001 d 1 E 6 | -0.02095 | -0.03896 – -0.00293 | -2.27831 | 0.02316980 |
| C Mdiploid 0.001 d 1 E 7 | 0.02727 | 0.00695 – 0.04759 | 2.63081 | 0.00880654 |
| C Mdiploid 0.001 d 1 E 8 | -0.00435 | -0.02578 – 0.01707 | -0.39806 | 0.69077548 |
| C Mdiploid 0.001 d 1 H 11 | 0.03103 | 0.00179 – 0.06027 | 2.07997 | 0.03808645 |
| C Mdiploid 0.001 d 1 H 2 | 0.01207 | -0.02316 – 0.04730 | 0.67164 | 0.50215015 |
| C Mdiploid 0.001 d 1 H 2 B | -0.01772 | -0.05147 – 0.01604 | -1.02856 | 0.30423060 |
| C Mdiploid 0.001 d 1 H 3 | -0.00572 | -0.04360 – 0.03215 | -0.29622 | 0.76719449 |
| C Mdiploid 0.001 d 1 H 3 B | 0.00004 | -0.03571 – 0.03580 | 0.00242 | 0.99807165 |
| C Mdiploid 0.001 d 1 H 4 | 0.00887 | -0.03993 – 0.05768 | 0.35635 | 0.72174082 |
| C Mdiploid 0.001 d 1 H 4 B | -0.00269 | -0.05249 – 0.04712 | -0.10576 | 0.91582228 |
| C Mdiploid 0.001 d 1 H 5 | 0.01359 | -0.01637 – 0.04355 | 0.88908 | 0.37442760 |
| C Mdiploid 0.001 d 1 H 5 B | -0.00275 | -0.02940 – 0.02390 | -0.20218 | 0.83986354 |
| C Mdiploid 0.001 d 1 H 6 | 0.03459 | 0.00779 – 0.06138 | 2.53005 | 0.01174027 |
| C Mdiploid 0.001 d 1 H 6 B | 0.02557 | -0.00048 – 0.05163 | 1.92371 | 0.05501270 |
| C Mdiploid 0.001 d 1 H 7 | 0.00943 | -0.04247 – 0.06132 | 0.35600 | 0.72200440 |
| C Mdiploid 0.001 d 1 H 8 | 0.01372 | -0.02957 – 0.05702 | 0.62116 | 0.53480274 |
| C Mdiploid 0.001 d 1 H 9 | 0.01923 | -0.02620 – 0.06465 | 0.82947 | 0.40727462 |
| C Mdiploid 0.004 d 1 A 2 | 0.00074 | -0.03253 – 0.03401 | 0.04350 | 0.96532350 |
| C Mdiploid 0.004 d 1 A 3 | 0.01219 | -0.02511 – 0.04950 | 0.64056 | 0.52212714 |
| C Mdiploid 0.004 d 1 A 4 | 0.05056 | 0.01662 – 0.08451 | 2.91976 | 0.00367654 |
| C Mdiploid 0.004 d 1 A 5 | 0.05149 | 0.01625 – 0.08674 | 2.86388 | 0.00437789 |
| C Mdiploid 0.004 d 1 A 6 | 0.01932 | -0.01445 – 0.05309 | 1.12129 | 0.26275545 |
| C Mdiploid 0.004 d 1 A 7 | 0.02866 | -0.01062 – 0.06794 | 1.42994 | 0.15341977 |
| C Mdiploid 0.004 d 1 B 3 | 0.02463 | -0.00370 – 0.05296 | 1.70377 | 0.08910621 |
| C Mdiploid 0.004 d 1 B 4 | 0.03125 | -0.00127 – 0.06378 | 1.88316 | 0.06031462 |
| C Mdiploid 0.004 d 1 B 5 | 0.04299 | 0.01619 – 0.06979 | 3.14407 | 0.00177490 |
| C Mdiploid 0.004 d 1 B 6 | 0.05328 | 0.01921 – 0.08734 | 3.06491 | 0.00230654 |
| C Mdiploid 0.004 d 1 B 7 | 0.07750 | 0.04758 – 0.10742 | 5.07684 | 0.00000056 |
| C Mdiploid 0.004 d 1 C 3 | 0.04274 | 0.01394 – 0.07154 | 2.90832 | 0.00381118 |
| C Mdiploid 0.004 d 1 C 4 | 0.02312 | -0.00410 – 0.05034 | 1.66497 | 0.09660494 |
| C Mdiploid 0.004 d 1 C 5 | 0.04335 | 0.01800 – 0.06869 | 3.35220 | 0.00086846 |
| C Mdiploid 0.004 d 1 C 6 | 0.06362 | 0.03622 – 0.09102 | 4.55122 | 0.00000685 |
| C Mdiploid 0.004 d 1 C 7 | 0.03836 | -0.01842 – 0.09514 | 1.32409 | 0.18613776 |
| C Mdiploid 0.004 d 1 H 2 | 0.02225 | -0.02299 – 0.06749 | 0.96402 | 0.33554719 |
| C Mdiploid 0.004 d 1 H 3 | 0.02107 | -0.02154 – 0.06367 | 0.96911 | 0.33300485 |
| C Mdiploid 0.004 d 1 H 4 | 0.00548 | -0.05419 – 0.06515 | 0.18007 | 0.85717391 |
| C Mdiploid 0.004 d 1 H 5 | 0.06027 | 0.02281 – 0.09772 | 3.15346 | 0.00171997 |
| C Mdiploid 0.004 d 1 H 6 | 0.04022 | 0.00560 – 0.07484 | 2.27725 | 0.02323376 |
| C Mdiploid 0.004 d 1 H 7 | 0.01165 | -0.05020 – 0.07350 | 0.36908 | 0.71224172 |
| C Mhaploid 0.001 d 1 A 2 | 0.17748 | -0.18514 – 0.54010 | 0.95929 | 0.33792087 |
| C Mhaploid 0.001 d 1 A 3 | 0.03476 | 0.00257 – 0.06694 | 2.11667 | 0.03482853 |
| C Mhaploid 0.001 d 1 A 3 B | 0.02653 | 0.00424 – 0.04882 | 2.33283 | 0.02009125 |
| C Mhaploid 0.001 d 1 A 4 | 0.07889 | 0.05620 – 0.10158 | 6.81600 | <0.001 |
| C Mhaploid 0.001 d 1 A 4 B | 0.00801 | -0.01550 – 0.03151 | 0.66754 | 0.50476447 |
| C Mhaploid 0.001 d 1 A 5 | 0.07567 | 0.04879 – 0.10255 | 5.51778 | 0.00000006 |
| C Mhaploid 0.001 d 1 A 5 B | 0.05377 | 0.02908 – 0.07845 | 4.26875 | 0.00002393 |
| C Mhaploid 0.001 d 1 A 6 | 0.02914 | 0.00850 – 0.04977 | 2.76747 | 0.00587909 |
| C Mhaploid 0.001 d 1 A 6 B | 0.02748 | 0.00967 – 0.04529 | 3.02424 | 0.00263330 |
| C Mhaploid 0.001 d 1 A 7 | 0.28425 | -0.16599 – 0.73448 | 1.23738 | 0.21658119 |
| C Mhaploid 0.001 d 1 A 7 B | 0.37549 | -0.17001 – 0.92100 | 1.34912 | 0.17796797 |
| C Mhaploid 0.001 d 1 H 2 | 0.05735 | 0.02810 – 0.08659 | 3.84317 | 0.00013869 |
| C Mhaploid 0.001 d 1 H 3 | 0.00589 | -0.02710 – 0.03888 | 0.34979 | 0.72665899 |
| C Mhaploid 0.001 d 1 H 3 B | 0.02272 | -0.01348 – 0.05892 | 1.23012 | 0.21928455 |
| C Mhaploid 0.001 d 1 H 4 | 0.06491 | -0.01016 – 0.13999 | 1.69462 | 0.09083022 |
| C Mhaploid 0.001 d 1 H 4 B | 0.01455 | -0.02446 – 0.05356 | 0.73088 | 0.46522961 |
| C Mhaploid 0.001 d 1 H 5 | 0.24925 | -0.16290 – 0.66141 | 1.18531 | 0.23651278 |
| C Mhaploid 0.001 d 1 H 5 B | 0.07769 | 0.04517 – 0.11021 | 4.68240 | 0.00000375 |
| C Mhaploid 0.001 d 1 H 6 | 0.10010 | 0.04219 – 0.15802 | 3.38783 | 0.00076555 |
| C Mhaploid 0.001 d 1 H 6 B | 0.11257 | 0.06724 – 0.15790 | 4.86715 | 0.00000156 |
| C Mhaploid 0.001 d 1 H 7 | 0.08866 | 0.04780 – 0.12952 | 4.25293 | 0.00002561 |
| C Mhaploid 0.001 d 1 H 7 B | 0.07939 | 0.04151 – 0.11728 | 4.10726 | 0.00004746 |
| Observations | 608 | |||
| R2 / R2 adjusted | 0.719 / 0.625 | |||
# -----------------------------------------------------------------------------
# create results dataframe
# ----------------------------------------------------
myres <- as.data.frame(coef(summary(mymod)), stringsAsFactors = F)
myres$p1s_l <- pt(coef(summary(mymod))[, 3], mymod$df, lower = T) # one-sided test p-values for lower
myres$p1s_h <- pt(coef(summary(mymod))[, 3], mymod$df, lower = F) # one-sided test p-values for higher
myres$p1s_l.adj <- p.adjust(myres$p1s_l, method = "fdr") # fdr adjusted p value (for 1-sided lower)
myres$p1s_h.adj <- p.adjust(myres$p1s_h, method = "fdr") # fdr adjusted p value (for 1-sided higher)
myres$bcid <- gsub("^.*?D", "", rownames(myres)) # fix barcode id names
myres$m_deltafit <- ddply(my, ~bctID, function(my) weighted.mean(my$deltafit,
my$rdeltafit))[, 2]
myres$m_rdeltafit <- ddply(my, ~bctID, function(my) mean(my$rdeltafit))[, 2]
myres$mcm_deltafit <- myres$m_deltafit - weighted.mean(myres$m_deltafit, myres$m_rdeltafit)
myres$se_deltafit <- ddply(my, ~bctID, function(my) sjstats::se(my$deltafit))[,
2]
myres$color <- "fdr > 0.01"
myres$color[myres$p1s_h.adj <= 0.01] <- "fdr <= 0.01"
myres$color <- as.factor(myres$color)
myres$color <- relevel(myres$color, "fdr > 0.01")
myres$treatment <- gsub("0025d", "0025", gsub("001d", "001", gsub(".{4}$", "",
myres$bcid))) # strip bc id from treatment label, store in myres
# -----------------------------------------------------------------------------
# review results
# --------------------------------------------------------------
paste0("weighted mean fitness increase for increasers = ", weighted.mean(myres$m_deltafit[myres$p1s_h.adj <=
0.01], myres$m_rdeltafit[myres$p1s_h.adj <= 0.01])) # weighted mean fit inc of sig increasers only
## [1] "weighted mean fitness increase for increasers = 0.0679894955523455"
paste0("minimum fitness increase for increasers = ", min(myres$m_deltafit[myres$p1s_h.adj <=
0.01])) # minimum significant increase of 2.4%
## [1] "minimum fitness increase for increasers = 0.0274814710527647"
paste0("maximum fitness increase for increasers = ", max(myres$m_deltafit[myres$p1s_h.adj <=
0.01])) # maximum significant increase of 23.5%
## [1] "maximum fitness increase for increasers = 0.234510778858253"
# all treatments together
paste0("Number of barcodes with significant decreases in fitness = ", nrow(myres[myres$p1s_l.adj <=
0.01, ])) # check for significant fitness decrease: # find none
## [1] "Number of barcodes with significant decreases in fitness = 0"
paste0("Number of barcodes with significant increases in fitness = ", nrow(myres[myres$p1s_h.adj <=
0.01, ])) # check for significant fitness increase: # find 35 rows. (35 increasers out of 152)
## [1] "Number of barcodes with significant increases in fitness = 35"
paste0("Proportion of barcodes with significant increases in fitness = ", nrow(myres[myres$p1s_h.adj <=
0.01, ])/nrow(myres)) # prop increasers # 0.23
## [1] "Proportion of barcodes with significant increases in fitness = 0.230263157894737"
# CM diploid 1:1000
paste0("CM diploid 1:1000:")
## [1] "CM diploid 1:1000:"
myrest <- myres[myres$treatment == "CMdiploid0.001", ]
paste0("n barcodes in treatment = ", nrow(myrest))
## [1] "n barcodes in treatment = 42"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 3"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.0714285714285714"
# CM haploid 1:1000
paste0("CM haploid 1:1000:")
## [1] "CM haploid 1:1000:"
myrest <- myres[myres$treatment == "CMhaploid0.001", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 10"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.454545454545455"
# CM+salt diploid 1:1000
paste0("CM+salt diploid 1:1000:")
## [1] "CM+salt diploid 1:1000:"
myrest <- myres[myres$treatment == "CM+Saltdiploid0.001", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 11"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.5"
# CM+ethanol diploid 1:1000
paste0("CM+ethanol diploid 1:1000:")
## [1] "CM+ethanol diploid 1:1000:"
myrest <- myres[myres$treatment == "CM+Ethanoldiploid0.001", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 2"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.0909090909090909"
# CM diploid 1:250
paste0("CM diploid 1:250:")
## [1] "CM diploid 1:250:"
myrest <- myres[myres$treatment == "CMdiploid0.004", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 9"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0.409090909090909"
# CM diploid 1:4000
paste0("CM diploid 1:4000:")
## [1] "CM diploid 1:4000:"
myrest <- myres[myres$treatment == "CMdiploid0.00025", ]
paste0("n entries in treatment = ", nrow(myrest))
## [1] "n entries in treatment = 22"
paste0("n barcodes with fitness increase in treatment = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ]))
## [1] "n barcodes with fitness increase in treatment = 0"
paste0("proportion of barcodes in treatment that increased in fitness = ", nrow(myrest[myrest$p1s_h.adj <=
0.01, ])/nrow(myrest))
## [1] "proportion of barcodes in treatment that increased in fitness = 0"
# -----------------------------------------------------------------------------
# Visualize
# ------------------------------------------------------------------- Plot
# ONLY data with se_deltafit < 0.029 for clarity -- This removes high SE
# points that have a large mean fitness increase, but have SE such that this
# fit increase is non-significant.
myx <- myres[myres$se_deltafit < 0.029, ]$m_deltafit
myr <- myres[myres$se_deltafit < 0.029, ]$m_rdeltafit
myc <- myres[myres$se_deltafit < 0.029, ]$color
mybinwidth <- 0.01
mytitle <- NULL
myylab <- "Count"
myxlab <- "Fitness change"
myhighlight <- "grey50"
myhighlight1 <- "black"
myhighlight2 <- setpalette[1]
myhighlight3 <- setpalette[2]
myfillapha <- 0.25
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.345
mywidth <- 3.345
p <- ggplot(myres[myres$se_deltafit < 0.029, ], aes(x = myx, color = myc, fill = myc)) +
geom_histogram(alpha = myfillapha, position = "identity", binwidth = mybinwidth) +
geom_vline(xintercept = weighted.mean(myx, myr), linetype = "dashed", color = myhighlight) +
geom_vline(xintercept = weighted.mean(myx[myc == "fdr > 0.01"], myr[myc ==
"fdr > 0.01"]), linetype = "dashed", color = myhighlight2) + geom_vline(xintercept = weighted.mean(myx[myc ==
"fdr <= 0.01"], myr[myc == "fdr <= 0.01"]), linetype = "dashed", color = myhighlight3) +
geom_rug(alpha = myptalpha) + scale_color_manual(values = mypalette, guide = FALSE) +
scale_fill_manual(values = mypalette) + theme_classic() + theme(legend.position = c(0.8,
0.8)) + labs(fill = "Significance") + ggtitle(mytitle) + ylab(myylab) +
xlab(myxlab) + geom_hline(yintercept = 0.01, colour = "white", size = 1.01) +
theme(axis.title.y = element_text(size = mytextsize, face = "bold", margin = margin(0,
0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = mytextsize,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = mytextsize,
angle = 0)) + theme(axis.text.x = element_text(size = mytextsize, angle = 0)) +
theme(legend.title = element_text(size = mytextsize, face = "bold")) + theme(legend.text = element_text(size = mytextsize)) +
scale_x_continuous(breaks = seq(-0.1, 0.25, by = 0.05), labels = c("-0.1",
"-0.05", "0", "0.05", "0.1", "0.15", "0.2", "0.25"), limits = c(-0.05,
0.25)) + scale_y_continuous(breaks = seq(0, 25, by = 5), limits = c(0,
25))
p
## Warning: Removed 4 rows containing missing values (geom_bar).
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Figure_4.pdf", width = mywidth, height = myheight)
p
## Warning: Removed 4 rows containing missing values (geom_bar).
dev.off() # 7by7
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, mymod2, myres, myrest, p)
rm(mybinwidth, myc, myfillapha, myheight, myhighlight, myhighlight1, myhighlight2,
myhighlight3, mylinealpha, mypalette, myptalpha, myr, mytextsize, mytitle,
mywidth, myx, myxlab, myylab)
Analysis: Characterization of error and contribution of covariates to SE deltafitness. 1: Build the dataframe to test SE delatfitness
my <- myfa # get a copy of the main data to work on.
# split by replicate and bind to form a wide frame.
my1 <- my[my$rep == 1, ] # r1
my2 <- my[my$rep == 2, ]
colnames(my2) <- paste0(colnames(my2), "_2") # r2
my3 <- my[my$rep == 3, ]
colnames(my3) <- paste0(colnames(my3), "_3") # r3
my4 <- my[my$rep == 4, ]
colnames(my4) <- paste0(colnames(my4), "_4") # r4
# sum(my1$bc1 != my2$bc1_2) # sort order matches.
my <- cbind(my1, my2, my3, my4) # bind
my$sedeltafit <- apply(cbind(my$deltafit, my$deltafit_2, my$deltafit_3, my$deltafit_4),
1, sjstats::se) # calculate SE deltafit among replicates
my$medcount <- apply(cbind(my$bc1cts_i_0r1, my$bc1cts_i_0r2, my$bc1cts_i_0r3,
my$bc1cts_i_0r4, my$bc1cts_f_0r1, my$bc1cts_f_0r2, my$bc1cts_f_0r3, my$bc1cts_f_0r4,
my$bc1cts_i, my$bc1cts_i_2, my$bc1cts_i_3, my$bc1cts_i_4, my$bc1cts_f, my$bc1cts_f_2,
my$bc1cts_f_3, my$bc1cts_f_4), 1, median, na.rm = T) # calculate median counts for each SE deltafit entry.
# Calculate BC proportions for next step @ all timepoints for all
# replicates.
my$prop_i_0r1 <- my$bc1cts_i_0r1/(my$bc1cts_i_0r1 + my$bc2cts_i_0r1)
my$prop_i_0r2 <- my$bc1cts_i_0r2/(my$bc1cts_i_0r2 + my$bc2cts_i_0r2)
my$prop_i_0r3 <- my$bc1cts_i_0r3/(my$bc1cts_i_0r3 + my$bc2cts_i_0r3)
my$prop_i_0r4 <- my$bc1cts_i_0r4/(my$bc1cts_i_0r4 + my$bc2cts_i_0r4)
my$prop_f_0r1 <- my$bc1cts_f_0r1/(my$bc1cts_f_0r1 + my$bc2cts_f_0r1)
my$prop_f_0r2 <- my$bc1cts_f_0r2/(my$bc1cts_f_0r2 + my$bc2cts_f_0r2)
my$prop_f_0r3 <- my$bc1cts_f_0r3/(my$bc1cts_f_0r3 + my$bc2cts_f_0r3)
my$prop_f_0r4 <- my$bc1cts_f_0r4/(my$bc1cts_f_0r4 + my$bc2cts_f_0r4)
my$prop_i_250r1 <- my$bc1cts_i/(my$bc1cts_i + my$bc2cts_i)
my$prop_i_250r2 <- my$bc1cts_i_2/(my$bc1cts_i_2 + my$bc2cts_i_2)
my$prop_i_250r3 <- my$bc1cts_i_3/(my$bc1cts_i_3 + my$bc2cts_i_3)
my$prop_i_250r4 <- my$bc1cts_i_4/(my$bc1cts_i_4 + my$bc2cts_i_4)
my$prop_f_250r1 <- my$bc1cts_f/(my$bc1cts_f + my$bc2cts_f)
my$prop_f_250r2 <- my$bc1cts_f_2/(my$bc1cts_f_2 + my$bc2cts_f_2)
my$prop_f_250r3 <- my$bc1cts_f_3/(my$bc1cts_f_3 + my$bc2cts_f_3)
my$prop_f_250r4 <- my$bc1cts_f_4/(my$bc1cts_f_4 + my$bc2cts_f_4)
my$medprop <- apply(cbind(my$prop_i_0r1, my$prop_i_0r2, my$prop_i_0r3, my$prop_i_0r4,
my$prop_f_0r1, my$prop_f_0r2, my$prop_f_0r3, my$prop_f_0r4, my$prop_i_250r1,
my$prop_i_250r2, my$prop_i_250r3, my$prop_i_250r4, my$prop_f_250r1, my$prop_f_250r2,
my$prop_f_250r3, my$prop_f_250r4), 1, median, na.rm = T) # calculate median proportion for each SE deltafit entry
my$mnfit0 <- NA # Calculate the weighted mean generation 0 fitness for each SE deltafitness entry
for (i in 1:nrow(my)) {
my$mnfit0[i] <- weighted.mean(my[i, c("fit0r1", "fit0r2", "fit0r3", "fit0r4")],
my[i, c("rfit0r1", "rfit0r2", "rfit0r3", "rfit0r4")], na.rm = T)
}
my$mnfit250 <- NA # Calculate the weighted mean generation 250 fitness for each SE deltafitness entry
for (i in 1:nrow(my)) {
my$mnfit250[i] <- weighted.mean(my[i, c("fit25", "fit25_2", "fit25_3", "fit25_4")],
my[i, c("rfit25", "rfit25_2", "rfit25_3", "rfit25_4")], na.rm = T)
}
# NOT USED IN MODEL-- captures same data as fit0 + fit250
my$mndeltafit <- NA
for (i in 1:nrow(my)) {
my$mndeltafit[i] <- weighted.mean(my[i, c("deltafit", "deltafit_2", "deltafit_3",
"deltafit_4")], my[i, c("rdeltafit", "rdeltafit_2", "rdeltafit_3", "rdeltafit_4")],
na.rm = T)
}
my$mnccdeltafit <- NA # Calculate the weighted mean cross contamination rate for each SE deltafitness entry
for (i in 1:nrow(my)) {
my$mnccdeltafit[i] <- weighted.mean(my[i, c("ccdeltafit", "ccdeltafit_2",
"ccdeltafit_3", "ccdeltafit_4")], my[i, c("rdeltafit", "rdeltafit_2",
"rdeltafit_3", "rdeltafit_4")], na.rm = T)
}
my$mnrdeltafit <- apply(cbind(my$rdeltafit, my$rdeltafit_2, my$rdeltafit_3,
my$rdeltafit_4), 1, mean, na.rm = T) # calculate an updated 'sampling effort' value for each SE deltafitness entry
myfa_w <- my
rm(my, my1, my2, my3, my4, i)
2: Assess variables that affect SE deltafitness: using sequential term removal via p-value
mymod <- lm(I(scale(myfa_w$sedeltafit)) ~ I(scale(myfa_w$mnccdeltafit)) + I(scale(myfa_w$mndeltafit)) +
I(scale(myfa_w$medcount)) + I(scale(myfa_w$medprop)) + myfa_w$treatment,
weights = myfa_w$mnrdeltafit)
car::vif(mymod) # VIF looks okay.
## GVIF Df GVIF^(1/(2*Df))
## I(scale(myfa_w$mnccdeltafit)) 1.819921 1 1.349045
## I(scale(myfa_w$mndeltafit)) 1.503512 1 1.226178
## I(scale(myfa_w$medcount)) 2.264308 1 1.504762
## I(scale(myfa_w$medprop)) 1.878926 1 1.370739
## myfa_w$treatment 3.261989 5 1.125507
summary(mymod)
##
## Call:
## lm(formula = I(scale(myfa_w$sedeltafit)) ~ I(scale(myfa_w$mnccdeltafit)) +
## I(scale(myfa_w$mndeltafit)) + I(scale(myfa_w$medcount)) +
## I(scale(myfa_w$medprop)) + myfa_w$treatment, weights = myfa_w$mnrdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -66.450 -18.399 -3.423 13.187 125.556
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -0.10948 0.08372 -1.308
## I(scale(myfa_w$mnccdeltafit)) 0.13240 0.05085 2.604
## I(scale(myfa_w$mndeltafit)) 0.31846 0.09076 3.509
## I(scale(myfa_w$medcount)) 0.04030 0.05250 0.768
## I(scale(myfa_w$medprop)) -0.10176 0.06031 -1.687
## myfa_w$treatmentCM+Ethanoldiploid0.001 0.14585 0.13639 1.069
## myfa_w$treatmentCM+Saltdiploid0.001 -0.08633 0.16839 -0.513
## myfa_w$treatmentCMdiploid0.00025 -0.02988 0.12585 -0.237
## myfa_w$treatmentCMdiploid0.004 -0.10866 0.14473 -0.751
## myfa_w$treatmentCMhaploid0.001 -0.06310 0.14083 -0.448
## Pr(>|t|)
## (Intercept) 0.193073
## I(scale(myfa_w$mnccdeltafit)) 0.010201 *
## I(scale(myfa_w$mndeltafit)) 0.000603 ***
## I(scale(myfa_w$medcount)) 0.444014
## I(scale(myfa_w$medprop)) 0.093743 .
## myfa_w$treatmentCM+Ethanoldiploid0.001 0.286752
## myfa_w$treatmentCM+Saltdiploid0.001 0.608977
## myfa_w$treatmentCMdiploid0.00025 0.812696
## myfa_w$treatmentCMdiploid0.004 0.454045
## myfa_w$treatmentCMhaploid0.001 0.654807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 30.44 on 142 degrees of freedom
## Multiple R-squared: 0.1379, Adjusted R-squared: 0.08322
## F-statistic: 2.523 on 9 and 142 DF, p-value: 0.01033
# model output
# -----------------------------------------------------------------=
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
show.r2 = F, file = "000_Additional_File_11.html") # r2 on the lm version of htis model is
|  | I(scale(myfa w\(sedeltafit))</th> </tr> <tr> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; text-align:left; ">Predictors</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Estimates</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">CI</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Statistic</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">p</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.10948</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.27356 – 0.05460</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-1.30774</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.19307337</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">I(scale(myfa_w\)mnccdeltafit)) | 0.13240 | 0.03274 – 0.23207 | 2.60374 | 0.01020124 | |||
|---|---|---|---|---|---|---|---|---|
| I(scale(myfa_w\(mndeltafit))</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.31846</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.14057 – 0.49635</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">3.50878</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00060335</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">I(scale(myfa_w\)medcount)) | 0.04030 | -0.06260 – 0.14320 | 0.76757 | 0.44401445 | ||||
| I(scale(myfa_w$medprop)) | -0.10176 | -0.21997 – 0.01644 | -1.68729 | 0.09374310 | ||||
| CM+Ethanoldiploid 0.001 | 0.14585 | -0.12148 – 0.41317 | 1.06929 | 0.28675155 | ||||
| CM+Saltdiploid 0.001 | -0.08633 | -0.41636 – 0.24371 | -0.51267 | 0.60897704 | ||||
| C Mdiploid 0.00025 | -0.02988 | -0.27653 – 0.21678 | -0.23739 | 0.81269600 | ||||
| C Mdiploid 0.004 | -0.10866 | -0.39232 – 0.17501 | -0.75075 | 0.45404536 | ||||
| C Mhaploid 0.001 | -0.06310 | -0.33913 – 0.21293 | -0.44804 | 0.65480662 | ||||
| Observations | 152 | |||||||
# -----------------------------------------------------------------------------
rm(mymod)
Analysis: Power (Look at the numbers)
# POC FIT ASSAY -- GET RMSE & EFFECTSIZE
# ---------------------------------------
my <- poc92[order(poc92$bcid), ] # get data
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r) # calculate mean corrected init fitness
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod) # model for rmse in POC assays
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~1.76 fitness change
myeffectsize <- 1/myrmse # effect size of 1 is a 1.76 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# ------------------------------------------------------------------------------
# POC FIT ASSAY -- CACULATE ESTIMATED POWER TO DETECT TREAT EFF IN NEXT EXP
# ---- power to detect treat effects
pow <- pwr.f2.test(u = 1, v = ((22 * 2) - 1 - 1), f2 = myeffectsize, sig.level = 0.05)
pow$power # 99.8% power to detect treatment fitness differences of 1.00%
## [1] 0.9982838
# ------------------------------------------------------------------------------
# POC FIT ASSAY -- CALCULATE POWER TO DETECT FITNESS DIFFERENCE FOR INDIV
# BCS --
my <- poc92
my1 <- my[my$exp == 1, ]
colnames(my1) <- paste0(colnames(my1), "_R1")
my2 <- my[my$exp == 2, ]
colnames(my2) <- paste0(colnames(my2), "_R2")
my3 <- my[my$exp == 3, ]
colnames(my3) <- paste0(colnames(my3), "_R3")
my4 <- my[my$exp == 4, ]
colnames(my4) <- paste0(colnames(my4), "_R4")
my5 <- my[my$exp == 5, ]
colnames(my5) <- paste0(colnames(my5), "_R5")
my6 <- my[my$exp == 6, ]
colnames(my6) <- paste0(colnames(my6), "_R6")
my7 <- my[my$exp == 7, ]
colnames(my7) <- paste0(colnames(my7), "_R7")
my8 <- my[my$exp == 8, ]
colnames(my8) <- paste0(colnames(my8), "_R8")
my9 <- my[my$exp == 9, ]
colnames(my9) <- paste0(colnames(my9), "_R9")
my10 <- my[my$exp == 10, ]
colnames(my10) <- paste0(colnames(my10), "_R10")
poc92_w <- cbind(my1, my2, my3, my4, my5, my6, my7, my8, my9, my10)
for (i in 1:nrow(poc92_w)) {
poc92_w$mnrw[i] <- weighted.mean(poc92_w[i, c("rw_R1", "rw_R2", "rw_R3",
"rw_R4", "rw_R5", "rw_R6", "rw_R7", "rw_R8", "rw_R9", "rw_R10")], poc92_w[i,
c("r_R1", "r_R2", "r_R3", "r_R4", "r_R5", "r_R6", "r_R7", "r_R8", "r_R9",
"r_R10")], na.rm = T)
poc92_w$mnr[i] <- rowMeans(poc92_w[i, c("r_R1", "r_R2", "r_R3", "r_R4",
"r_R5", "r_R6", "r_R7", "r_R8", "r_R9", "r_R10")], na.rm = T)
}
my <- poc92_w
my$psd_r1 <- ((my$rw_R1 - my$mnrw)^2)/10
my$psd_r2 <- ((my$rw_R2 - my$mnrw)^2)/10
my$psd_r3 <- ((my$rw_R3 - my$mnrw)^2)/10
my$psd_r4 <- ((my$rw_R4 - my$mnrw)^2)/10
my$psd_r5 <- ((my$rw_R5 - my$mnrw)^2)/10
my$psd_r6 <- ((my$rw_R6 - my$mnrw)^2)/10
my$psd_r7 <- ((my$rw_R7 - my$mnrw)^2)/10
my$psd_r8 <- ((my$rw_R8 - my$mnrw)^2)/10
my$psd_r9 <- ((my$rw_R9 - my$mnrw)^2)/10
my$psd_r10 <- ((my$rw_R10 - my$mnrw)^2)/10
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4, my$psd_r5,
my$psd_r6, my$psd_r7, my$psd_r8, my$psd_r9, my$psd_r10), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnr, na.rm = T)
pow <- pwr.t.test(d = 0.01/psd, n = 4, sig.level = 0.05)
pow$power # 67% power to detect 1% fit change.
## [1] 0.6669863
pow <- pwr.t.test(power = 0.8, n = 4, sig.level = 0.05)
pow$d * psd # 80% power to detect ~1.2% fitness difference
## [1] 0.01176284
# ------------------------------------------------------------------------------
# 250 GEN EXP -- GET RMSE & EFFECTSIZE
# -----------------------------------------
my <- myfa[order(myfa$bctID), ]
mymod <- lm(my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
summary(mymod)
##
## Call:
## lm(formula = my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## my$bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## my$bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## my$bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## my$bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## my$bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## my$bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## my$bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## my$bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## my$bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## my$bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## my$bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## my$bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## my$bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## my$bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## my$bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## my$bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## my$bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## my$bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## my$bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## my$bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## my$bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## my$bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## my$bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## my$bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## my$bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## my$bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## my$bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## my$bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## my$bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## my$bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## my$bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## my$bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## my$bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## my$bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## my$bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## my$bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## my$bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## my$bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## my$bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## my$bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## my$bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## my$bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## my$bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## my$bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## my$bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## my$bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## my$bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## my$bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## my$bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## my$bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## my$bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## my$bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## my$bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## my$bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## my$bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## my$bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## my$bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## my$bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## my$bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## my$bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## my$bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## my$bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## my$bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## my$bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## my$bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## my$bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## my$bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## my$bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## my$bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## my$bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## my$bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## my$bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## my$bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## my$bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## my$bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## my$bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## my$bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## my$bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## my$bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## my$bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## my$bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## my$bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## my$bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## my$bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## my$bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## my$bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## my$bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## my$bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## my$bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## my$bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## my$bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## my$bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## my$bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## my$bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## my$bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## my$bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## my$bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## my$bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## my$bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## my$bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## my$bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## my$bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## my$bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## my$bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## my$bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## my$bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## my$bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## my$bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## my$bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## my$bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## my$bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## my$bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## my$bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## my$bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## my$bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## my$bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## my$bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## my$bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## my$bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## my$bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## my$bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## my$bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## my$bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## my$bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## my$bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## my$bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## my$bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## my$bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## my$bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## my$bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## my$bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## my$bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## my$bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## my$bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## my$bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## my$bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## my$bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## my$bctIDCM+Saltdiploid0.001d1A3 0.153770
## my$bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## my$bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## my$bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## my$bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## my$bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## my$bctIDCM+Saltdiploid0.001d1F5 0.208173
## my$bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## my$bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## my$bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## my$bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## my$bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## my$bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## my$bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## my$bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## my$bctIDCM+Saltdiploid0.001d1H2 0.747562
## my$bctIDCM+Saltdiploid0.001d1H3 0.191568
## my$bctIDCM+Saltdiploid0.001d1H4 0.269265
## my$bctIDCM+Saltdiploid0.001d1H5 0.767557
## my$bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## my$bctIDCMdiploid0.00025d1A2 0.329882
## my$bctIDCMdiploid0.00025d1A3 0.557181
## my$bctIDCMdiploid0.00025d1A4 0.903664
## my$bctIDCMdiploid0.00025d1A5 0.077226 .
## my$bctIDCMdiploid0.00025d1A6 0.358086
## my$bctIDCMdiploid0.00025d1A7 0.786112
## my$bctIDCMdiploid0.00025d1B10 0.624463
## my$bctIDCMdiploid0.00025d1B11 0.166007
## my$bctIDCMdiploid0.00025d1B12 0.103764
## my$bctIDCMdiploid0.00025d1B9 0.107440
## my$bctIDCMdiploid0.00025d1C10 0.368373
## my$bctIDCMdiploid0.00025d1C11 0.048000 *
## my$bctIDCMdiploid0.00025d1C12 0.259774
## my$bctIDCMdiploid0.00025d1C9 0.662997
## my$bctIDCMdiploid0.00025d1D2 0.946757
## my$bctIDCMdiploid0.00025d1E2 0.221035
## my$bctIDCMdiploid0.00025d1H2 0.666246
## my$bctIDCMdiploid0.00025d1H3 0.792714
## my$bctIDCMdiploid0.00025d1H4 0.447657
## my$bctIDCMdiploid0.00025d1H5 0.065659 .
## my$bctIDCMdiploid0.00025d1H6 0.759244
## my$bctIDCMdiploid0.00025d1H7 0.945372
## my$bctIDCMdiploid0.001d1A11 0.047218 *
## my$bctIDCMdiploid0.001d1A2 0.568387
## my$bctIDCMdiploid0.001d1A2B 0.445453
## my$bctIDCMdiploid0.001d1A3 0.982727
## my$bctIDCMdiploid0.001d1A3B 0.774047
## my$bctIDCMdiploid0.001d1A4 0.174949
## my$bctIDCMdiploid0.001d1A4B 0.908856
## my$bctIDCMdiploid0.001d1A5 0.061817 .
## my$bctIDCMdiploid0.001d1A5B 0.726909
## my$bctIDCMdiploid0.001d1A6 0.074694 .
## my$bctIDCMdiploid0.001d1A6B 0.476265
## my$bctIDCMdiploid0.001d1A7 0.001518 **
## my$bctIDCMdiploid0.001d1A8 0.196716
## my$bctIDCMdiploid0.001d1A9 0.044951 *
## my$bctIDCMdiploid0.001d1B1 0.006576 **
## my$bctIDCMdiploid0.001d1B2 0.003039 **
## my$bctIDCMdiploid0.001d1C1 0.002576 **
## my$bctIDCMdiploid0.001d1C2 0.013632 *
## my$bctIDCMdiploid0.001d1D4 0.096546 .
## my$bctIDCMdiploid0.001d1D5 0.152415
## my$bctIDCMdiploid0.001d1D6 0.315226
## my$bctIDCMdiploid0.001d1D7 0.310496
## my$bctIDCMdiploid0.001d1D8 0.240516
## my$bctIDCMdiploid0.001d1E4 0.700111
## my$bctIDCMdiploid0.001d1E5 0.197395
## my$bctIDCMdiploid0.001d1E6 0.023170 *
## my$bctIDCMdiploid0.001d1E7 0.008807 **
## my$bctIDCMdiploid0.001d1E8 0.690775
## my$bctIDCMdiploid0.001d1H11 0.038086 *
## my$bctIDCMdiploid0.001d1H2 0.502150
## my$bctIDCMdiploid0.001d1H2B 0.304231
## my$bctIDCMdiploid0.001d1H3 0.767194
## my$bctIDCMdiploid0.001d1H3B 0.998072
## my$bctIDCMdiploid0.001d1H4 0.721741
## my$bctIDCMdiploid0.001d1H4B 0.915822
## my$bctIDCMdiploid0.001d1H5 0.374428
## my$bctIDCMdiploid0.001d1H5B 0.839864
## my$bctIDCMdiploid0.001d1H6 0.011740 *
## my$bctIDCMdiploid0.001d1H6B 0.055013 .
## my$bctIDCMdiploid0.001d1H7 0.722004
## my$bctIDCMdiploid0.001d1H8 0.534803
## my$bctIDCMdiploid0.001d1H9 0.407275
## my$bctIDCMdiploid0.004d1A2 0.965323
## my$bctIDCMdiploid0.004d1A3 0.522127
## my$bctIDCMdiploid0.004d1A4 0.003677 **
## my$bctIDCMdiploid0.004d1A5 0.004378 **
## my$bctIDCMdiploid0.004d1A6 0.262755
## my$bctIDCMdiploid0.004d1A7 0.153420
## my$bctIDCMdiploid0.004d1B3 0.089106 .
## my$bctIDCMdiploid0.004d1B4 0.060315 .
## my$bctIDCMdiploid0.004d1B5 0.001775 **
## my$bctIDCMdiploid0.004d1B6 0.002307 **
## my$bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## my$bctIDCMdiploid0.004d1C3 0.003811 **
## my$bctIDCMdiploid0.004d1C4 0.096605 .
## my$bctIDCMdiploid0.004d1C5 0.000868 ***
## my$bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## my$bctIDCMdiploid0.004d1C7 0.186138
## my$bctIDCMdiploid0.004d1H2 0.335547
## my$bctIDCMdiploid0.004d1H3 0.333005
## my$bctIDCMdiploid0.004d1H4 0.857174
## my$bctIDCMdiploid0.004d1H5 0.001720 **
## my$bctIDCMdiploid0.004d1H6 0.023234 *
## my$bctIDCMdiploid0.004d1H7 0.712242
## my$bctIDCMhaploid0.001d1A2 0.337921
## my$bctIDCMhaploid0.001d1A3 0.034829 *
## my$bctIDCMhaploid0.001d1A3B 0.020091 *
## my$bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## my$bctIDCMhaploid0.001d1A4B 0.504764
## my$bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## my$bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## my$bctIDCMhaploid0.001d1A6 0.005879 **
## my$bctIDCMhaploid0.001d1A6B 0.002633 **
## my$bctIDCMhaploid0.001d1A7 0.216581
## my$bctIDCMhaploid0.001d1A7B 0.177968
## my$bctIDCMhaploid0.001d1H2 0.000139 ***
## my$bctIDCMhaploid0.001d1H3 0.726659
## my$bctIDCMhaploid0.001d1H3B 0.219285
## my$bctIDCMhaploid0.001d1H4 0.090830 .
## my$bctIDCMhaploid0.001d1H4B 0.465230
## my$bctIDCMhaploid0.001d1H5 0.236513
## my$bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## my$bctIDCMhaploid0.001d1H6 0.000766 ***
## my$bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## my$bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## my$bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# ------------------------------------------------------------------------------
# 250 GEN EXP -- CACULATE POWER TO DETECT TREAT EFF
# ---------------------------
pow <- pwr.f2.test(u = 1, v = ((22 * 2) - 1 - 1), f2 = myeffectsize, sig.level = 0.05)
pow$power # 87.7% power to detect treatment fitness differences of 1.00%
## [1] 0.8774087
# ------------------------------------------------------------------------------
# 250 GEN EXP -- CACULATE POWER TO DETECT TREAT EFF
# ---------------------------
pow <- pwr.f2.test(u = 1, v = ((22 * 2) - 1 - 1), power = 0.8, sig.level = 0.05)
pow$f2 * myrmse # 80% power to detect 0.81% fitness difference
## [1] 0.805129
# ------------------------------------------------------------------------------
# 250 GEN EXP -- CALCULATE POWER TO DETECT INDIV BC FIT CHANGE
# ----------------- power to detect fitness increase / decrease (t-tests) d
# = m1 - m2 / q; m1 is mean group 1, m2 is mean group 2, q is common
# standard deviation in the two groups here m1 is change in fitness and m2
# is 0. mean q is used and is the weighted mean of all population standard
# deviation of the four replicate sets in each row of the dataset.
my <- myfa_w
my$psd_r1 <- ((my$deltafit - my$mndeltafit)^2)/4
my$psd_r2 <- ((my$deltafit_2 - my$mndeltafit)^2)/4
my$psd_r3 <- ((my$deltafit_3 - my$mndeltafit)^2)/4
my$psd_r4 <- ((my$deltafit_4 - my$mndeltafit)^2)/4
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnrdeltafit, na.rm = T)
pow <- pwr.t.test(d = 0.01/psd, n = 4, sig.level = 0.05)
pow # 16% power to detect 1% fit change.
##
## Two-sample t test power calculation
##
## n = 4
## d = 0.7962996
## sig.level = 0.05
## power = 0.1590779
## alternative = two.sided
##
## NOTE: n is number in *each* group
pow <- pwr.t.test(power = 0.8, n = 4, sig.level = 0.05)
pow$d * psd # 80% power to detect ~3% fitness change
## [1] 0.02989775
# ------------------------------------------------------------------------------
# take a look at gen0 and gen250 rmse in the 250 gen exp
# -----------------------
my <- myfa[order(myfa$bctID), ]
mymod <- lm(my$fit25 ~ my$bctID + 0, weights = my$rfit25)
summary(mymod)
##
## Call:
## lm(formula = my$fit25 ~ my$bctID + 0, weights = my$rfit25)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -9.1715 -0.9752 0.0414 0.9554 6.1788
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.963451 0.013867 69.480
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.984867 0.015701 62.727
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.986599 0.017651 55.896
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.973802 0.012858 75.737
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.966859 0.018424 52.478
## my$bctIDCM+Ethanoldiploid0.001d1D10 1.010873 0.009826 102.872
## my$bctIDCM+Ethanoldiploid0.001d1D11 1.024829 0.010728 95.533
## my$bctIDCM+Ethanoldiploid0.001d1D12 1.014725 0.008440 120.232
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.985883 0.027027 36.477
## my$bctIDCM+Ethanoldiploid0.001d1E10 1.032632 0.010304 100.216
## my$bctIDCM+Ethanoldiploid0.001d1E11 1.002435 0.011071 90.546
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.969248 0.009034 107.292
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.982459 0.008004 122.741
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.986787 0.007737 127.542
## my$bctIDCM+Ethanoldiploid0.001d1F2 1.006324 0.010337 97.351
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.981219 0.014219 69.007
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.985948 0.016376 60.207
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.967685 0.019741 49.018
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.985630 0.026969 36.547
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.966028 0.015492 62.357
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.963587 0.014233 67.699
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.972524 0.027256 35.682
## my$bctIDCM+Saltdiploid0.001d1A2 1.003080 0.013592 73.797
## my$bctIDCM+Saltdiploid0.001d1A3 0.976835 0.011965 81.642
## my$bctIDCM+Saltdiploid0.001d1A4 0.995006 0.012603 78.949
## my$bctIDCM+Saltdiploid0.001d1A5 1.367358 0.297833 4.591
## my$bctIDCM+Saltdiploid0.001d1A7 0.984799 0.013335 73.848
## my$bctIDCM+Saltdiploid0.001d1F3 1.073742 0.014402 74.555
## my$bctIDCM+Saltdiploid0.001d1F4 1.102326 0.014096 78.200
## my$bctIDCM+Saltdiploid0.001d1F5 1.020556 0.235137 4.340
## my$bctIDCM+Saltdiploid0.001d1F6 1.157105 0.009150 126.455
## my$bctIDCM+Saltdiploid0.001d1F7 1.090952 0.011076 98.495
## my$bctIDCM+Saltdiploid0.001d1F8 1.049813 0.015578 67.393
## my$bctIDCM+Saltdiploid0.001d1G3 1.049051 0.054696 19.180
## my$bctIDCM+Saltdiploid0.001d1G4 1.076757 0.063848 16.864
## my$bctIDCM+Saltdiploid0.001d1G5 1.025620 0.013611 75.351
## my$bctIDCM+Saltdiploid0.001d1G6 1.045501 0.041898 24.953
## my$bctIDCM+Saltdiploid0.001d1G7 1.029401 0.023183 44.403
## my$bctIDCM+Saltdiploid0.001d1G8 1.071798 0.042388 25.286
## my$bctIDCM+Saltdiploid0.001d1H2 0.957811 0.014499 66.061
## my$bctIDCM+Saltdiploid0.001d1H3 0.982402 0.016191 60.674
## my$bctIDCM+Saltdiploid0.001d1H4 0.987646 0.019389 50.937
## my$bctIDCM+Saltdiploid0.001d1H5 0.974183 0.011405 85.416
## my$bctIDCM+Saltdiploid0.001d1H7 1.045882 0.031565 33.135
## my$bctIDCMdiploid0.00025d1A2 1.037815 0.020402 50.868
## my$bctIDCMdiploid0.00025d1A3 1.001131 0.017966 55.724
## my$bctIDCMdiploid0.00025d1A4 1.019341 0.016584 61.465
## my$bctIDCMdiploid0.00025d1A5 1.041482 0.017911 58.148
## my$bctIDCMdiploid0.00025d1A6 1.045937 0.015753 66.398
## my$bctIDCMdiploid0.00025d1A7 1.006742 0.018831 53.463
## my$bctIDCMdiploid0.00025d1B10 0.999307 0.016570 60.309
## my$bctIDCMdiploid0.00025d1B11 1.028195 0.012627 81.426
## my$bctIDCMdiploid0.00025d1B12 1.035979 0.022594 45.852
## my$bctIDCMdiploid0.00025d1B9 1.021965 0.014339 71.270
## my$bctIDCMdiploid0.00025d1C10 0.985098 0.019799 49.756
## my$bctIDCMdiploid0.00025d1C11 1.031708 0.018783 54.927
## my$bctIDCMdiploid0.00025d1C12 1.019119 0.013479 75.609
## my$bctIDCMdiploid0.00025d1C9 1.011106 0.013032 77.587
## my$bctIDCMdiploid0.00025d1D2 1.013528 0.062000 16.347
## my$bctIDCMdiploid0.00025d1E2 1.029348 0.011151 92.311
## my$bctIDCMdiploid0.00025d1H2 1.020608 0.016743 60.956
## my$bctIDCMdiploid0.00025d1H3 0.994000 0.018657 53.278
## my$bctIDCMdiploid0.00025d1H4 1.021795 0.026975 37.880
## my$bctIDCMdiploid0.00025d1H5 1.040704 0.014802 70.311
## my$bctIDCMdiploid0.00025d1H6 1.025827 0.014200 72.244
## my$bctIDCMdiploid0.00025d1H7 0.984556 0.032873 29.950
## my$bctIDCMdiploid0.001d1A11 1.007766 0.017154 58.748
## my$bctIDCMdiploid0.001d1A2 0.986372 0.014003 70.443
## my$bctIDCMdiploid0.001d1A2B 0.986861 0.012407 79.542
## my$bctIDCMdiploid0.001d1A3 0.976045 0.015296 63.810
## my$bctIDCMdiploid0.001d1A3B 0.979348 0.014503 67.526
## my$bctIDCMdiploid0.001d1A4 0.984315 0.012202 80.667
## my$bctIDCMdiploid0.001d1A4B 0.989793 0.013660 72.459
## my$bctIDCMdiploid0.001d1A5 0.993992 0.016109 61.703
## my$bctIDCMdiploid0.001d1A5B 0.980431 0.015626 62.744
## my$bctIDCMdiploid0.001d1A6 1.013207 0.014253 71.085
## my$bctIDCMdiploid0.001d1A6B 0.999566 0.015638 63.920
## my$bctIDCMdiploid0.001d1A7 1.025543 0.012948 79.206
## my$bctIDCMdiploid0.001d1A8 0.993437 0.011119 89.343
## my$bctIDCMdiploid0.001d1A9 0.986097 0.010649 92.604
## my$bctIDCMdiploid0.001d1B1 1.016269 0.016999 59.786
## my$bctIDCMdiploid0.001d1B2 1.002337 0.014509 69.083
## my$bctIDCMdiploid0.001d1C1 1.020060 0.014315 71.258
## my$bctIDCMdiploid0.001d1C2 1.017595 0.013774 73.881
## my$bctIDCMdiploid0.001d1D4 1.067399 0.074890 14.253
## my$bctIDCMdiploid0.001d1D5 0.979841 0.012312 79.587
## my$bctIDCMdiploid0.001d1D6 1.046058 0.058480 17.887
## my$bctIDCMdiploid0.001d1D7 1.014950 0.013558 74.857
## my$bctIDCMdiploid0.001d1D8 0.997020 0.011230 88.784
## my$bctIDCMdiploid0.001d1E4 1.010603 0.006826 148.044
## my$bctIDCMdiploid0.001d1E5 1.033504 0.038763 26.662
## my$bctIDCMdiploid0.001d1E6 0.993830 0.008161 121.773
## my$bctIDCMdiploid0.001d1E7 1.020338 0.011981 85.160
## my$bctIDCMdiploid0.001d1E8 1.002793 0.011770 85.196
## my$bctIDCMdiploid0.001d1H11 1.005581 0.014066 71.489
## my$bctIDCMdiploid0.001d1H2 0.983175 0.015550 63.225
## my$bctIDCMdiploid0.001d1H2B 0.973202 0.017121 56.842
## my$bctIDCMdiploid0.001d1H3 0.955747 0.019100 50.040
## my$bctIDCMdiploid0.001d1H3B 0.975072 0.017222 56.619
## my$bctIDCMdiploid0.001d1H4 0.970652 0.023820 40.750
## my$bctIDCMdiploid0.001d1H4B 0.996744 0.026152 38.114
## my$bctIDCMdiploid0.001d1H5 0.988919 0.014746 67.064
## my$bctIDCMdiploid0.001d1H5B 0.965644 0.014078 68.590
## my$bctIDCMdiploid0.001d1H6 0.991813 0.013183 75.235
## my$bctIDCMdiploid0.001d1H6B 0.991850 0.013283 74.670
## my$bctIDCMdiploid0.001d1H7 1.003503 0.030378 33.034
## my$bctIDCMdiploid0.001d1H8 0.992926 0.020850 47.622
## my$bctIDCMdiploid0.001d1H9 0.971339 0.021763 44.632
## my$bctIDCMdiploid0.004d1A2 0.988981 0.013667 72.362
## my$bctIDCMdiploid0.004d1A3 0.984729 0.016141 61.009
## my$bctIDCMdiploid0.004d1A4 0.994079 0.015905 62.499
## my$bctIDCMdiploid0.004d1A5 0.997874 0.016986 58.748
## my$bctIDCMdiploid0.004d1A6 1.027650 0.014870 69.107
## my$bctIDCMdiploid0.004d1A7 0.992307 0.017317 57.304
## my$bctIDCMdiploid0.004d1B3 0.988934 0.014756 67.019
## my$bctIDCMdiploid0.004d1B4 0.985693 0.015316 64.357
## my$bctIDCMdiploid0.004d1B5 1.000996 0.012416 80.622
## my$bctIDCMdiploid0.004d1B6 1.003170 0.017641 56.865
## my$bctIDCMdiploid0.004d1B7 1.045935 0.014064 74.370
## my$bctIDCMdiploid0.004d1C3 1.028783 0.012579 81.788
## my$bctIDCMdiploid0.004d1C4 0.987677 0.012745 77.496
## my$bctIDCMdiploid0.004d1C5 1.013335 0.010636 95.277
## my$bctIDCMdiploid0.004d1C6 1.042827 0.010406 100.210
## my$bctIDCMdiploid0.004d1C7 0.997839 0.028501 35.010
## my$bctIDCMdiploid0.004d1H2 0.980742 0.018749 52.309
## my$bctIDCMdiploid0.004d1H3 0.956690 0.019174 49.894
## my$bctIDCMdiploid0.004d1H4 0.976638 0.025731 37.956
## my$bctIDCMdiploid0.004d1H5 1.009555 0.016716 60.396
## my$bctIDCMdiploid0.004d1H6 1.002083 0.013995 71.601
## my$bctIDCMdiploid0.004d1H7 0.952544 0.033334 28.576
## my$bctIDCMhaploid0.001d1A2 1.157512 0.279919 4.135
## my$bctIDCMhaploid0.001d1A3 1.013898 0.019112 53.051
## my$bctIDCMhaploid0.001d1A3B 1.014289 0.011345 89.402
## my$bctIDCMhaploid0.001d1A4 1.054419 0.011371 92.729
## my$bctIDCMhaploid0.001d1A4B 0.998726 0.014140 70.632
## my$bctIDCMhaploid0.001d1A5 1.056410 0.012143 86.994
## my$bctIDCMhaploid0.001d1A5B 1.045252 0.012263 85.237
## my$bctIDCMhaploid0.001d1A6 1.016519 0.008791 115.632
## my$bctIDCMhaploid0.001d1A6B 1.029186 0.008495 121.151
## my$bctIDCMhaploid0.001d1A7 1.275039 0.347685 3.667
## my$bctIDCMhaploid0.001d1A7B 1.363221 0.421763 3.232
## my$bctIDCMhaploid0.001d1H2 1.059241 0.012838 82.510
## my$bctIDCMhaploid0.001d1H3 0.988700 0.018482 53.497
## my$bctIDCMhaploid0.001d1H3B 1.005140 0.023529 42.719
## my$bctIDCMhaploid0.001d1H4 1.050420 0.051175 20.526
## my$bctIDCMhaploid0.001d1H4B 1.016370 0.021185 47.976
## my$bctIDCMhaploid0.001d1H5 1.235717 0.318538 3.879
## my$bctIDCMhaploid0.001d1H5B 1.074655 0.022350 48.083
## my$bctIDCMhaploid0.001d1H6 1.091139 0.041404 26.353
## my$bctIDCMhaploid0.001d1H6B 1.095044 0.032821 33.364
## my$bctIDCMhaploid0.001d1H7 1.087144 0.018430 58.987
## my$bctIDCMhaploid0.001d1H7B 1.072426 0.019800 54.162
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A4 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A5 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A6 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A7 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D10 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D11 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D12 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D9 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E10 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E12 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E9 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1F1 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1F2 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1G1 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1G2 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H2 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H4 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H5 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H6 < 0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H7 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A2 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A3 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A4 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A5 0.00000571 ***
## my$bctIDCM+Saltdiploid0.001d1A7 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F3 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F4 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F5 0.00001754 ***
## my$bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F8 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G3 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G4 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G5 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G7 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G8 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H2 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H3 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H4 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H5 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B10 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B11 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B12 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B9 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C10 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C11 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C12 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C9 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1D2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1E2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A11 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A2B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A3B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A4B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A5B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A6B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A8 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A9 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1B1 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1B2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1C1 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1C2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D8 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E8 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H11 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H2B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H3B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H4B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H5B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H6B < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H8 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H9 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C7 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H2 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H3 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H4 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H5 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H6 < 0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H7 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A2 0.00004223 ***
## my$bctIDCMhaploid0.001d1A3 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A3B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A4 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A4B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A5 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A5B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A6 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A6B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A7 0.000274 ***
## my$bctIDCMhaploid0.001d1A7B 0.001317 **
## my$bctIDCMhaploid0.001d1H2 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H3 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H3B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H4 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H4B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H5 0.000120 ***
## my$bctIDCMhaploid0.001d1H5B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H6 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H6B < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H7 < 0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H7B < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.061 on 456 degrees of freedom
## Multiple R-squared: 0.9994, Adjusted R-squared: 0.9992
## F-statistic: 4789 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
my <- myfa[order(myfa$bctID), ]
colnames(my)
## [1] "etp" "rep" "medium" "ploidy"
## [5] "dilution" "set" "wo" "ref"
## [9] "bc1" "bc2" "ppects_i" "ppccts_i"
## [13] "cc_i" "refcts_i" "bc1cts_i" "bc2cts_i"
## [17] "ppects_f" "ppccts_f" "cc_f" "refcts_f"
## [21] "bc1cts_f" "bc2cts_f" "ppects_i_0r1" "ppccts_i_0r1"
## [25] "cc_i_0r1" "refcts_i_0r1" "bc1cts_i_0r1" "bc2cts_i_0r1"
## [29] "ppects_f_0r1" "ppccts_f_0r1" "cc_f_0r1" "refcts_f_0r1"
## [33] "bc1cts_f_0r1" "bc2cts_f_0r1" "ppects_i_0r2" "ppccts_i_0r2"
## [37] "cc_i_0r2" "refcts_i_0r2" "bc1cts_i_0r2" "bc2cts_i_0r2"
## [41] "ppects_f_0r2" "ppccts_f_0r2" "cc_f_0r2" "refcts_f_0r2"
## [45] "bc1cts_f_0r2" "bc2cts_f_0r2" "ppects_i_0r3" "ppccts_i_0r3"
## [49] "cc_i_0r3" "refcts_i_0r3" "bc1cts_i_0r3" "bc2cts_i_0r3"
## [53] "ppects_f_0r3" "ppccts_f_0r3" "cc_f_0r3" "refcts_f_0r3"
## [57] "bc1cts_f_0r3" "bc2cts_f_0r3" "ppects_i_0r4" "ppccts_i_0r4"
## [61] "cc_i_0r4" "refcts_i_0r4" "bc1cts_i_0r4" "bc2cts_i_0r4"
## [65] "ppects_f_0r4" "ppccts_f_0r4" "cc_f_0r4" "refcts_f_0r4"
## [69] "bc1cts_f_0r4" "bc2cts_f_0r4" "treatment" "bctID"
## [73] "well" "r0ir1" "r0ir2" "r0ir3"
## [77] "r0ir4" "r0fr1" "r0fr2" "r0fr3"
## [81] "r0fr4" "rfit0r1" "rfit0r2" "rfit0r3"
## [85] "rfit0r4" "rfit0" "r25i" "r25f"
## [89] "rfit25" "rdeltafit" "ccfit0r1" "ccfit0r2"
## [93] "ccfit0r3" "ccfit0r4" "ccfit0" "ccfit25"
## [97] "ccdeltafit" "fit0r1" "fit0r2" "fit0r3"
## [101] "fit0r4" "fit0" "fit25" "deltafit"
my$fit0_rep <- NA
my$rfit0_rep <- NA
my$fit0_rep[my$rep == 1] <- my$fit0r1[my$rep == 1]
my$fit0_rep[my$rep == 2] <- my$fit0r2[my$rep == 2]
my$fit0_rep[my$rep == 3] <- my$fit0r3[my$rep == 3]
my$fit0_rep[my$rep == 4] <- my$fit0r4[my$rep == 4]
my$rfit0_rep[my$rep == 1] <- my$rfit0r1[my$rep == 1]
my$rfit0_rep[my$rep == 2] <- my$rfit0r2[my$rep == 2]
my$rfit0_rep[my$rep == 3] <- my$rfit0r3[my$rep == 3]
my$rfit0_rep[my$rep == 4] <- my$rfit0r4[my$rep == 4]
mymod <- lm(my$fit0_rep ~ my$bctID + 0, weights = my$rfit0_rep)
summary(mymod)
##
## Call:
## lm(formula = my$fit0_rep ~ my$bctID + 0, weights = my$rfit0_rep)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.5475 -1.2523 -0.1441 0.8667 5.4984
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.962494 0.010580 90.97
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.968545 0.011510 84.14
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.990136 0.014092 70.26
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.965589 0.010077 95.82
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.960576 0.013070 73.49
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.969441 0.010839 89.44
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.992719 0.010547 94.12
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.988082 0.010884 90.78
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.959631 0.012389 77.46
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.981753 0.011559 84.94
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.967834 0.011247 86.05
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.997350 0.013737 72.60
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.990882 0.012137 81.64
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.971274 0.009290 104.55
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.984080 0.011572 85.04
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.970851 0.014848 65.39
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.998316 0.018337 54.44
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.982819 0.017066 57.59
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.982828 0.020489 47.97
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.962950 0.012147 79.27
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.960609 0.011481 83.67
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.983603 0.023197 42.40
## my$bctIDCM+Saltdiploid0.001d1A2 0.955986 0.010410 91.84
## my$bctIDCM+Saltdiploid0.001d1A3 0.958286 0.011246 85.21
## my$bctIDCM+Saltdiploid0.001d1A4 0.965260 0.011629 83.01
## my$bctIDCM+Saltdiploid0.001d1A5 0.968864 0.012579 77.03
## my$bctIDCM+Saltdiploid0.001d1A7 0.953495 0.012767 74.68
## my$bctIDCM+Saltdiploid0.001d1F3 0.932474 0.023796 39.19
## my$bctIDCM+Saltdiploid0.001d1F4 0.914693 0.027816 32.88
## my$bctIDCM+Saltdiploid0.001d1F5 0.822687 0.024127 34.10
## my$bctIDCM+Saltdiploid0.001d1F6 0.928939 0.017860 52.01
## my$bctIDCM+Saltdiploid0.001d1F7 0.939325 0.026384 35.60
## my$bctIDCM+Saltdiploid0.001d1F8 0.920819 0.018910 48.69
## my$bctIDCM+Saltdiploid0.001d1G3 0.946621 0.019711 48.02
## my$bctIDCM+Saltdiploid0.001d1G4 0.941129 0.020410 46.11
## my$bctIDCM+Saltdiploid0.001d1G5 0.930304 0.022667 41.04
## my$bctIDCM+Saltdiploid0.001d1G6 0.952838 0.022767 41.85
## my$bctIDCM+Saltdiploid0.001d1G7 0.938790 0.018583 50.52
## my$bctIDCM+Saltdiploid0.001d1G8 0.943725 0.022474 41.99
## my$bctIDCM+Saltdiploid0.001d1H2 0.948592 0.015571 60.92
## my$bctIDCM+Saltdiploid0.001d1H3 0.956984 0.015343 62.37
## my$bctIDCM+Saltdiploid0.001d1H4 0.961809 0.018594 51.73
## my$bctIDCM+Saltdiploid0.001d1H5 0.963743 0.012152 79.31
## my$bctIDCM+Saltdiploid0.001d1H7 0.952408 0.018999 50.13
## my$bctIDCMdiploid0.00025d1A2 1.022006 0.011427 89.44
## my$bctIDCMdiploid0.00025d1A3 1.008993 0.011612 86.89
## my$bctIDCMdiploid0.00025d1A4 1.017824 0.012270 82.95
## my$bctIDCMdiploid0.00025d1A5 1.014867 0.012455 81.48
## my$bctIDCMdiploid0.00025d1A6 1.034698 0.010426 99.24
## my$bctIDCMdiploid0.00025d1A7 1.008659 0.013031 77.40
## my$bctIDCMdiploid0.00025d1B10 1.005398 0.016316 61.62
## my$bctIDCMdiploid0.00025d1B11 1.009439 0.012185 82.84
## my$bctIDCMdiploid0.00025d1B12 1.004495 0.013986 71.82
## my$bctIDCMdiploid0.00025d1B9 0.999090 0.012437 80.33
## my$bctIDCMdiploid0.00025d1C10 0.999352 0.017402 57.43
## my$bctIDCMdiploid0.00025d1C11 0.999118 0.013849 72.15
## my$bctIDCMdiploid0.00025d1C12 1.001326 0.012925 77.47
## my$bctIDCMdiploid0.00025d1C9 1.015090 0.010821 93.81
## my$bctIDCMdiploid0.00025d1D2 1.004189 0.048243 20.82
## my$bctIDCMdiploid0.00025d1E2 1.014989 0.009891 102.62
## my$bctIDCMdiploid0.00025d1H2 1.002867 0.016977 59.07
## my$bctIDCMdiploid0.00025d1H3 0.998372 0.016109 61.98
## my$bctIDCMdiploid0.00025d1H4 1.003253 0.021693 46.25
## my$bctIDCMdiploid0.00025d1H5 1.014345 0.012614 80.41
## my$bctIDCMdiploid0.00025d1H6 1.020570 0.012701 80.35
## my$bctIDCMdiploid0.00025d1H7 0.983039 0.023109 42.54
## my$bctIDCMdiploid0.001d1A11 0.970366 0.018533 52.36
## my$bctIDCMdiploid0.001d1A2 0.975007 0.013275 73.45
## my$bctIDCMdiploid0.001d1A2B 0.974784 0.011536 84.50
## my$bctIDCMdiploid0.001d1A3 0.972302 0.013676 71.09
## my$bctIDCMdiploid0.001d1A3B 0.974165 0.012749 76.41
## my$bctIDCMdiploid0.001d1A4 0.961181 0.012944 74.26
## my$bctIDCMdiploid0.001d1A4B 0.984780 0.012782 77.05
## my$bctIDCMdiploid0.001d1A5 0.961848 0.014602 65.87
## my$bctIDCMdiploid0.001d1A5B 0.983279 0.013397 73.39
## my$bctIDCMdiploid0.001d1A6 0.988556 0.011435 86.45
## my$bctIDCMdiploid0.001d1A6B 0.990352 0.010730 92.30
## my$bctIDCMdiploid0.001d1A7 0.979856 0.013057 75.04
## my$bctIDCMdiploid0.001d1A8 0.975804 0.012011 81.24
## my$bctIDCMdiploid0.001d1A9 0.961569 0.011644 82.58
## my$bctIDCMdiploid0.001d1B1 0.958865 0.020395 47.02
## my$bctIDCMdiploid0.001d1B2 0.960968 0.012761 75.31
## my$bctIDCMdiploid0.001d1C1 0.958228 0.020354 47.08
## my$bctIDCMdiploid0.001d1C2 0.983893 0.012147 81.00
## my$bctIDCMdiploid0.001d1D4 0.977347 0.020614 47.41
## my$bctIDCMdiploid0.001d1D5 0.995926 0.013524 73.64
## my$bctIDCMdiploid0.001d1D6 1.004007 0.017032 58.95
## my$bctIDCMdiploid0.001d1D7 1.001532 0.010581 94.65
## my$bctIDCMdiploid0.001d1D8 1.009267 0.010403 97.01
## my$bctIDCMdiploid0.001d1E4 1.005912 0.008459 118.91
## my$bctIDCMdiploid0.001d1E5 0.995704 0.015351 64.86
## my$bctIDCMdiploid0.001d1E6 1.012643 0.009825 103.07
## my$bctIDCMdiploid0.001d1E7 0.992462 0.008874 111.83
## my$bctIDCMdiploid0.001d1E8 1.004870 0.010131 99.18
## my$bctIDCMdiploid0.001d1H11 0.976808 0.014251 68.54
## my$bctIDCMdiploid0.001d1H2 0.962956 0.018381 52.39
## my$bctIDCMdiploid0.001d1H2B 0.989311 0.016404 60.31
## my$bctIDCMdiploid0.001d1H3 0.962012 0.017526 54.89
## my$bctIDCMdiploid0.001d1H3B 0.973909 0.018316 53.17
## my$bctIDCMdiploid0.001d1H4 0.962188 0.023860 40.33
## my$bctIDCMdiploid0.001d1H4B 1.001834 0.023090 43.39
## my$bctIDCMdiploid0.001d1H5 0.970515 0.014772 65.70
## my$bctIDCMdiploid0.001d1H5B 0.968555 0.012466 77.69
## my$bctIDCMdiploid0.001d1H6 0.949987 0.013255 71.67
## my$bctIDCMdiploid0.001d1H6B 0.965792 0.012520 77.14
## my$bctIDCMdiploid0.001d1H7 0.993479 0.021999 45.16
## my$bctIDCMdiploid0.001d1H8 0.977389 0.022839 42.80
## my$bctIDCMdiploid0.001d1H9 0.950019 0.023675 40.13
## my$bctIDCMdiploid0.004d1A2 0.985606 0.018858 52.27
## my$bctIDCMdiploid0.004d1A3 0.976035 0.019883 49.09
## my$bctIDCMdiploid0.004d1A4 0.944859 0.017743 53.25
## my$bctIDCMdiploid0.004d1A5 0.945876 0.018431 51.32
## my$bctIDCMdiploid0.004d1A6 1.002603 0.018105 55.38
## my$bctIDCMdiploid0.004d1A7 0.955714 0.021399 44.66
## my$bctIDCMdiploid0.004d1B3 0.963265 0.014146 68.09
## my$bctIDCMdiploid0.004d1B4 0.952382 0.017395 54.75
## my$bctIDCMdiploid0.004d1B5 0.956015 0.014282 66.94
## my$bctIDCMdiploid0.004d1B6 0.949296 0.017072 55.60
## my$bctIDCMdiploid0.004d1B7 0.966761 0.015592 62.00
## my$bctIDCMdiploid0.004d1C3 0.981819 0.015965 61.50
## my$bctIDCMdiploid0.004d1C4 0.962429 0.014530 66.24
## my$bctIDCMdiploid0.004d1C5 0.963746 0.014279 67.49
## my$bctIDCMdiploid0.004d1C6 0.973028 0.016034 60.69
## my$bctIDCMdiploid0.004d1C7 0.959283 0.028626 33.51
## my$bctIDCMdiploid0.004d1H2 0.959636 0.024803 38.69
## my$bctIDCMdiploid0.004d1H3 0.936685 0.023040 40.66
## my$bctIDCMdiploid0.004d1H4 0.969610 0.033178 29.22
## my$bctIDCMdiploid0.004d1H5 0.951522 0.019214 49.52
## my$bctIDCMdiploid0.004d1H6 0.960859 0.019822 48.47
## my$bctIDCMdiploid0.004d1H7 0.940943 0.029734 31.64
## my$bctIDCMhaploid0.001d1A2 0.980436 0.011797 83.11
## my$bctIDCMhaploid0.001d1A3 0.978873 0.013796 70.96
## my$bctIDCMhaploid0.001d1A3B 0.986977 0.011368 86.82
## my$bctIDCMhaploid0.001d1A4 0.973858 0.011619 83.81
## my$bctIDCMhaploid0.001d1A4B 0.990814 0.010013 98.95
## my$bctIDCMhaploid0.001d1A5 0.980078 0.014499 67.59
## my$bctIDCMhaploid0.001d1A5B 0.994215 0.012566 79.12
## my$bctIDCMhaploid0.001d1A6 0.987998 0.011618 85.04
## my$bctIDCMhaploid0.001d1A6B 1.001544 0.009495 105.48
## my$bctIDCMhaploid0.001d1A7 0.990798 0.016061 61.69
## my$bctIDCMhaploid0.001d1A7B 0.987728 0.013270 74.43
## my$bctIDCMhaploid0.001d1H2 1.001457 0.016052 62.39
## my$bctIDCMhaploid0.001d1H3 0.982176 0.015353 63.97
## my$bctIDCMhaploid0.001d1H3B 0.983050 0.012886 76.29
## my$bctIDCMhaploid0.001d1H4 0.984402 0.020589 47.81
## my$bctIDCMhaploid0.001d1H4B 1.001499 0.018799 53.27
## my$bctIDCMhaploid0.001d1H5 0.986595 0.011444 86.21
## my$bctIDCMhaploid0.001d1H5B 0.997087 0.010052 99.20
## my$bctIDCMhaploid0.001d1H6 0.990876 0.012487 79.35
## my$bctIDCMhaploid0.001d1H6B 0.984733 0.010481 93.96
## my$bctIDCMhaploid0.001d1H7 0.997542 0.021955 45.44
## my$bctIDCMhaploid0.001d1H7B 0.993393 0.018373 54.07
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A4 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A5 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A6 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1A7 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D10 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D11 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D12 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1D9 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E10 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E12 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1E9 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1F1 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1F2 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1G1 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1G2 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H2 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H4 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H5 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H6 <0.0000000000000002 ***
## my$bctIDCM+Ethanoldiploid0.001d1H7 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A2 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A3 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A4 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A5 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1A7 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F3 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F4 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F5 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F6 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F8 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G3 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G4 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G5 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G6 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G7 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1G8 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H2 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H3 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H4 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H5 <0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1H7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1A7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B10 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B11 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B12 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1B9 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C10 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C11 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C12 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1C9 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1D2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1E2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.00025d1H7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A11 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A2B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A3B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A4B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A5B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A6B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A8 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1A9 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1B1 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1B2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1C1 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1C2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1D8 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1E8 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H11 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H2B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H3B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H4B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H5B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H6B <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H8 <0.0000000000000002 ***
## my$bctIDCMdiploid0.001d1H9 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1A7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1B7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1C7 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H2 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H3 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H4 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H5 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H6 <0.0000000000000002 ***
## my$bctIDCMdiploid0.004d1H7 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A2 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A3 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A3B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A4 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A4B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A5 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A5B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A6 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A6B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A7 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1A7B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H2 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H3 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H3B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H4 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H4B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H5 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H5B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H6 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H6B <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H7 <0.0000000000000002 ***
## my$bctIDCMhaploid0.001d1H7B <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.805 on 456 degrees of freedom
## Multiple R-squared: 0.9994, Adjusted R-squared: 0.9992
## F-statistic: 5001 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
rm(my, my1, my10, my2, my3, my4, my5, my6, my7, my8, my9, mymod, pow, i, myeffectsize,
myrmse, psd)
Analysis: Power (Table Output) + Commented Code to produce POWER SURFACE WITH VARIABLE N (COMMENTED VIZ NOT USED IN MANUSCRIPT – uncomment all commented lines here to create an alternative figure_3 with additional data included)
# POC DATA
# -----------------------------------------------------------------------------------------
# individual bc
# ----------------------------------------------------------------
my <- poc92 # conduct necessary calculations
my1 <- my[my$exp == 1, ]
colnames(my1) <- paste0(colnames(my1), "_R1")
my2 <- my[my$exp == 2, ]
colnames(my2) <- paste0(colnames(my2), "_R2")
my3 <- my[my$exp == 3, ]
colnames(my3) <- paste0(colnames(my3), "_R3")
my4 <- my[my$exp == 4, ]
colnames(my4) <- paste0(colnames(my4), "_R4")
my5 <- my[my$exp == 5, ]
colnames(my5) <- paste0(colnames(my5), "_R5")
my6 <- my[my$exp == 6, ]
colnames(my6) <- paste0(colnames(my6), "_R6")
my7 <- my[my$exp == 7, ]
colnames(my7) <- paste0(colnames(my7), "_R7")
my8 <- my[my$exp == 8, ]
colnames(my8) <- paste0(colnames(my8), "_R8")
my9 <- my[my$exp == 9, ]
colnames(my9) <- paste0(colnames(my9), "_R9")
my10 <- my[my$exp == 10, ]
colnames(my10) <- paste0(colnames(my10), "_R10")
poc92_w <- cbind(my1, my2, my3, my4, my5, my6, my7, my8, my9, my10)
for (i in 1:nrow(poc92_w)) {
poc92_w$mnrw[i] <- weighted.mean(poc92_w[i, c("rw_R1", "rw_R2", "rw_R3",
"rw_R4", "rw_R5", "rw_R6", "rw_R7", "rw_R8", "rw_R9", "rw_R10")], poc92_w[i,
c("r_R1", "r_R2", "r_R3", "r_R4", "r_R5", "r_R6", "r_R7", "r_R8", "r_R9",
"r_R10")], na.rm = T)
poc92_w$mnr[i] <- rowMeans(poc92_w[i, c("r_R1", "r_R2", "r_R3", "r_R4",
"r_R5", "r_R6", "r_R7", "r_R8", "r_R9", "r_R10")], na.rm = T)
}
my <- poc92_w
my$psd_r1 <- ((my$rw_R1 - my$mnrw)^2)/10
my$psd_r2 <- ((my$rw_R2 - my$mnrw)^2)/10
my$psd_r3 <- ((my$rw_R3 - my$mnrw)^2)/10
my$psd_r4 <- ((my$rw_R4 - my$mnrw)^2)/10
my$psd_r5 <- ((my$rw_R5 - my$mnrw)^2)/10
my$psd_r6 <- ((my$rw_R6 - my$mnrw)^2)/10
my$psd_r7 <- ((my$rw_R7 - my$mnrw)^2)/10
my$psd_r8 <- ((my$rw_R8 - my$mnrw)^2)/10
my$psd_r9 <- ((my$rw_R9 - my$mnrw)^2)/10
my$psd_r10 <- ((my$rw_R10 - my$mnrw)^2)/10
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4, my$psd_r5,
my$psd_r6, my$psd_r7, my$psd_r8, my$psd_r9, my$psd_r10), na.rm = T)
my$psd <- sqrt(my$psd_m) # get the pop standard dev for each entry
psd <- weighted.mean(my$psd, my$mnr, na.rm = T) # get the weighted mean pop standard dev for reporting
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 22 * 3, ncol = 5)
colnames(mypower) <- c("fitchange", "psd", "n", "power", "sig.level")
mypower <- as.data.frame(mypower)
mypower$fitchange <- c(0.0025, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035,
0.04, 0.045, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.14, 0.16, 0.18,
0.2, 0.22)
mypower$psd <- psd
mypower$n <- c(rep(2, 22), rep(3, 22), rep(4, 22))
mypower$power <- NA
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 22), rep(2, 22), rep(3, 22))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.t.test(d = mypower$fitchange[i]/mypower$psd[i], n = mypower$n[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_5.html")
| fitchange | psd | n | power | sig.level |
|---|---|---|---|---|
| 0.0025 | 0.00494 | 2 | 0.06178 | 0.05 |
| 0.005 | 0.00494 | 2 | 0.09626 | 0.05 |
| 0.01 | 0.00494 | 2 | 0.22197 | 0.05 |
| 0.015 | 0.00494 | 2 | 0.39385 | 0.05 |
| 0.02 | 0.00494 | 2 | 0.57263 | 0.05 |
| 0.025 | 0.00494 | 2 | 0.72731 | 0.05 |
| 0.03 | 0.00494 | 2 | 0.84254 | 0.05 |
| 0.035 | 0.00494 | 2 | 0.91772 | 0.05 |
| 0.04 | 0.00494 | 2 | 0.96109 | 0.05 |
| 0.045 | 0.00494 | 2 | 0.98335 | 0.05 |
| 0.05 | 0.00494 | 2 | 0.99355 | 0.05 |
| 0.06 | 0.00494 | 2 | 0.99928 | 0.05 |
| 0.07 | 0.00494 | 2 | 0.99995 | 0.05 |
| 0.08 | 0.00494 | 2 | 1 | 0.05 |
| 0.09 | 0.00494 | 2 | 1 | 0.05 |
| 0.1 | 0.00494 | 2 | 1 | 0.05 |
| 0.12 | 0.00494 | 2 | 1 | 0.05 |
| 0.14 | 0.00494 | 2 | 1 | 0.05 |
| 0.16 | 0.00494 | 2 | 1 | 0.05 |
| 0.18 | 0.00494 | 2 | 1 | 0.05 |
| 0.2 | 0.00494 | 2 | 1 | 0.05 |
| 0.22 | 0.00494 | 2 | 1 | 0.05 |
| 0.0025 | 0.00494 | 3 | 0.0775 | 0.05 |
| 0.005 | 0.00494 | 3 | 0.16143 | 0.05 |
| 0.01 | 0.00494 | 3 | 0.47113 | 0.05 |
| 0.015 | 0.00494 | 3 | 0.79151 | 0.05 |
| 0.02 | 0.00494 | 3 | 0.95204 | 0.05 |
| 0.025 | 0.00494 | 3 | 0.99369 | 0.05 |
| 0.03 | 0.00494 | 3 | 0.99953 | 0.05 |
| 0.035 | 0.00494 | 3 | 0.99998 | 0.05 |
| 0.04 | 0.00494 | 3 | 1 | 0.05 |
| 0.045 | 0.00494 | 3 | 1 | 0.05 |
| 0.05 | 0.00494 | 3 | 1 | 0.05 |
| 0.06 | 0.00494 | 3 | 1 | 0.05 |
| 0.07 | 0.00494 | 3 | 1 | 0.05 |
| 0.08 | 0.00494 | 3 | 1 | 0.05 |
| 0.09 | 0.00494 | 3 | 1 | 0.05 |
| 0.1 | 0.00494 | 3 | 1 | 0.05 |
| 0.12 | 0.00494 | 3 | 1 | 0.05 |
| 0.14 | 0.00494 | 3 | 1 | 0.05 |
| 0.16 | 0.00494 | 3 | 1 | 0.05 |
| 0.18 | 0.00494 | 3 | 1 | 0.05 |
| 0.2 | 0.00494 | 3 | 1 | 0.05 |
| 0.22 | 0.00494 | 3 | 1 | 0.05 |
| 0.0025 | 0.00494 | 4 | 0.09332 | 0.05 |
| 0.005 | 0.00494 | 4 | 0.22739 | 0.05 |
| 0.01 | 0.00494 | 4 | 0.66699 | 0.05 |
| 0.015 | 0.00494 | 4 | 0.94369 | 0.05 |
| 0.02 | 0.00494 | 4 | 0.99672 | 0.05 |
| 0.025 | 0.00494 | 4 | 0.99994 | 0.05 |
| 0.03 | 0.00494 | 4 | 1 | 0.05 |
| 0.035 | 0.00494 | 4 | 1 | 0.05 |
| 0.04 | 0.00494 | 4 | 1 | 0.05 |
| 0.045 | 0.00494 | 4 | 1 | 0.05 |
| 0.05 | 0.00494 | 4 | 1 | 0.05 |
| 0.06 | 0.00494 | 4 | 1 | 0.05 |
| 0.07 | 0.00494 | 4 | 1 | 0.05 |
| 0.08 | 0.00494 | 4 | 1 | 0.05 |
| 0.09 | 0.00494 | 4 | 1 | 0.05 |
| 0.1 | 0.00494 | 4 | 1 | 0.05 |
| 0.12 | 0.00494 | 4 | 1 | 0.05 |
| 0.14 | 0.00494 | 4 | 1 | 0.05 |
| 0.16 | 0.00494 | 4 | 1 | 0.05 |
| 0.18 | 0.00494 | 4 | 1 | 0.05 |
| 0.2 | 0.00494 | 4 | 1 | 0.05 |
| 0.22 | 0.00494 | 4 | 1 | 0.05 |
# -----------------------------------------------------------------------------
# # Plotting ------------------------------------------------- # plot effect
# size x power ------------- mypower$n <- factor(mypower$n, levels =
# c(4,3,2)) p<-ggplot(mypower, aes(x=fitchange, y=power, group=n)) +
# geom_vline(xintercept = 0.01, linetype = 'dashed', color = 'gray')+
# geom_vline(xintercept = 0.025, linetype = 'dashed', color = 'gray')+
# geom_vline(xintercept = 0.05, linetype = 'dashed', color = 'gray')+
# geom_vline(xintercept = 0.1, linetype = 'dashed', color = 'gray')+
# geom_line(aes(color=n))+ geom_point(aes(color=n))+ geom_point(x = 0.01, y
# = pwr.t.test(d = 0.01 / psd, n = 4, sig.level = 0.05)$power, pch = 1, size
# = 5, color = 'gray')+ geom_point(x = 0.025, y = pwr.t.test(d = 0.025 /
# psd, n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = 'gray')+
# geom_point(x = 0.05, y = pwr.t.test(d = 0.05 / psd, n = 4, sig.level =
# 0.05)$power, pch = 1, size = 5, color = 'gray')+ geom_point(x = 0.1, y =
# pwr.t.test(d = 0.1 / psd, n = 4, sig.level = 0.05)$power, pch = 1, size =
# 5, color = 'gray')+ scale_x_continuous(name = '', breaks = c(0.01, 0.025,
# 0.05, 0.1), labels = c('1%', '2.5%', '5%', '10%'), limits = c(0, 0.1))+
# labs(color = 'n (replicates \n per timepoint)')+ theme_classic()+
# labs(tag = 'A')+ theme(legend.position = 'none')+ theme(axis.text.x =
# element_text(angle = 45, hjust = 1)) p pA <- p
# treatment
# -------------------------------------------------------------------- run
# the appropriate model (fit equivalence model from above -- telling us
# ability to measure fit for each bcid based on our 10 replicates.)
my <- poc92[order(poc92$bcid), ]
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r)
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod)
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~1.76 fitness change
myeffectsize <- 1/myrmse # effect size of 1 is a 1.76 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
myv <- (22 * 2) - 1 - 1
mypowersize <- (pwr.f2.test(u = 1, v = myv, f2 = myeffectsize, sig.level = 0.05))$power # powersize for plotting below, where 22 is the number of BCs in any given treatment.
# prepare plot data ----------
mypower <- matrix(nrow = 100, ncol = 5)
colnames(mypower) <- c("u", "v", "power", "f2", "sig.level")
mypower <- as.data.frame(mypower)
mypower$u <- 1
mypower$v <- c(myv, myv * 0.9, myv * 0.8, myv * 0.7, myv * 0.6, myv * 0.5, myv *
0.4, myv * 0.3, myv * 0.2, myv * 0.1)
mypower$power <- NA
mypower$f2 <- c(rep(myeffectsize * 2, 10), rep(myeffectsize * 1.75, 10), rep(myeffectsize *
1.5, 10), rep(myeffectsize * 1.25, 10), rep(myeffectsize * 1, 10), rep(myeffectsize *
0.75, 10), rep(myeffectsize * 0.5, 10), rep(myeffectsize * 0.25, 10), rep(myeffectsize *
0.1, 10), rep(myeffectsize * 0.05, 10))
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10), rep(5, 10),
rep(6, 10), rep(7, 10), rep(8, 10), rep(9, 10), rep(10, 10))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.f2.test(u = mypower$u[i], v = mypower$v[i], f2 = mypower$f2[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_6.html")
| u | v | power | f2 | sig.level |
|---|---|---|---|---|
| 1 | 42 | 1 | 1.13786 | 0.05 |
| 1 | 37.8 | 1 | 1.13786 | 0.05 |
| 1 | 33.6 | 0.99999 | 1.13786 | 0.05 |
| 1 | 29.4 | 0.99993 | 1.13786 | 0.05 |
| 1 | 25.2 | 0.99964 | 1.13786 | 0.05 |
| 1 | 21 | 0.9982 | 1.13786 | 0.05 |
| 1 | 16.8 | 0.99152 | 1.13786 | 0.05 |
| 1 | 12.6 | 0.96323 | 1.13786 | 0.05 |
| 1 | 8.4 | 0.85736 | 1.13786 | 0.05 |
| 1 | 4.2 | 0.53379 | 1.13786 | 0.05 |
| 1 | 42 | 1 | 0.99563 | 0.05 |
| 1 | 37.8 | 0.99998 | 0.99563 | 0.05 |
| 1 | 33.6 | 0.99993 | 0.99563 | 0.05 |
| 1 | 29.4 | 0.99971 | 0.99563 | 0.05 |
| 1 | 25.2 | 0.99881 | 0.99563 | 0.05 |
| 1 | 21 | 0.9953 | 0.99563 | 0.05 |
| 1 | 16.8 | 0.98249 | 0.99563 | 0.05 |
| 1 | 12.6 | 0.93932 | 0.99563 | 0.05 |
| 1 | 8.4 | 0.80939 | 0.99563 | 0.05 |
| 1 | 4.2 | 0.48329 | 0.99563 | 0.05 |
| 1 | 42 | 0.99997 | 0.85339 | 0.05 |
| 1 | 37.8 | 0.9999 | 0.85339 | 0.05 |
| 1 | 33.6 | 0.99965 | 0.85339 | 0.05 |
| 1 | 29.4 | 0.99882 | 0.85339 | 0.05 |
| 1 | 25.2 | 0.99618 | 0.85339 | 0.05 |
| 1 | 21 | 0.98809 | 0.85339 | 0.05 |
| 1 | 16.8 | 0.96467 | 0.85339 | 0.05 |
| 1 | 12.6 | 0.90157 | 0.85339 | 0.05 |
| 1 | 8.4 | 0.74793 | 0.85339 | 0.05 |
| 1 | 4.2 | 0.42907 | 0.85339 | 0.05 |
| 1 | 42 | 0.99977 | 0.71116 | 0.05 |
| 1 | 37.8 | 0.99936 | 0.71116 | 0.05 |
| 1 | 33.6 | 0.99827 | 0.71116 | 0.05 |
| 1 | 29.4 | 0.99542 | 0.71116 | 0.05 |
| 1 | 25.2 | 0.98825 | 0.71116 | 0.05 |
| 1 | 21 | 0.97084 | 0.71116 | 0.05 |
| 1 | 16.8 | 0.93065 | 0.71116 | 0.05 |
| 1 | 12.6 | 0.84354 | 0.71116 | 0.05 |
| 1 | 8.4 | 0.67065 | 0.71116 | 0.05 |
| 1 | 4.2 | 0.3713 | 0.71116 | 0.05 |
| 1 | 42 | 0.99828 | 0.56893 | 0.05 |
| 1 | 37.8 | 0.99626 | 0.56893 | 0.05 |
| 1 | 33.6 | 0.99199 | 0.56893 | 0.05 |
| 1 | 29.4 | 0.98321 | 0.56893 | 0.05 |
| 1 | 25.2 | 0.96562 | 0.56893 | 0.05 |
| 1 | 21 | 0.93157 | 0.56893 | 0.05 |
| 1 | 16.8 | 0.86837 | 0.56893 | 0.05 |
| 1 | 12.6 | 0.75723 | 0.56893 | 0.05 |
| 1 | 8.4 | 0.57569 | 0.56893 | 0.05 |
| 1 | 4.2 | 0.31033 | 0.56893 | 0.05 |
| 1 | 42 | 0.98846 | 0.4267 | 0.05 |
| 1 | 37.8 | 0.98002 | 0.4267 | 0.05 |
| 1 | 33.6 | 0.96592 | 0.4267 | 0.05 |
| 1 | 29.4 | 0.94277 | 0.4267 | 0.05 |
| 1 | 25.2 | 0.90564 | 0.4267 | 0.05 |
| 1 | 21 | 0.84773 | 0.4267 | 0.05 |
| 1 | 16.8 | 0.76039 | 0.4267 | 0.05 |
| 1 | 12.6 | 0.63426 | 0.4267 | 0.05 |
| 1 | 8.4 | 0.46235 | 0.4267 | 0.05 |
| 1 | 4.2 | 0.24671 | 0.4267 | 0.05 |
| 1 | 42 | 0.93266 | 0.28446 | 0.05 |
| 1 | 37.8 | 0.90628 | 0.28446 | 0.05 |
| 1 | 33.6 | 0.87079 | 0.28446 | 0.05 |
| 1 | 29.4 | 0.82377 | 0.28446 | 0.05 |
| 1 | 25.2 | 0.7625 | 0.28446 | 0.05 |
| 1 | 21 | 0.68427 | 0.28446 | 0.05 |
| 1 | 16.8 | 0.58681 | 0.28446 | 0.05 |
| 1 | 12.6 | 0.46905 | 0.28446 | 0.05 |
| 1 | 8.4 | 0.33223 | 0.28446 | 0.05 |
| 1 | 4.2 | 0.18127 | 0.28446 | 0.05 |
| 1 | 42 | 0.68587 | 0.14223 | 0.05 |
| 1 | 37.8 | 0.64003 | 0.14223 | 0.05 |
| 1 | 33.6 | 0.58928 | 0.14223 | 0.05 |
| 1 | 29.4 | 0.53353 | 0.14223 | 0.05 |
| 1 | 25.2 | 0.47288 | 0.14223 | 0.05 |
| 1 | 21 | 0.40761 | 0.14223 | 0.05 |
| 1 | 16.8 | 0.33826 | 0.14223 | 0.05 |
| 1 | 12.6 | 0.26565 | 0.14223 | 0.05 |
| 1 | 8.4 | 0.19088 | 0.14223 | 0.05 |
| 1 | 4.2 | 0.11516 | 0.14223 | 0.05 |
| 1 | 42 | 0.33969 | 0.05689 | 0.05 |
| 1 | 37.8 | 0.31119 | 0.05689 | 0.05 |
| 1 | 33.6 | 0.28227 | 0.05689 | 0.05 |
| 1 | 29.4 | 0.25301 | 0.05689 | 0.05 |
| 1 | 25.2 | 0.22351 | 0.05689 | 0.05 |
| 1 | 21 | 0.19387 | 0.05689 | 0.05 |
| 1 | 16.8 | 0.16421 | 0.05689 | 0.05 |
| 1 | 12.6 | 0.13464 | 0.05689 | 0.05 |
| 1 | 8.4 | 0.10521 | 0.05689 | 0.05 |
| 1 | 4.2 | 0.07583 | 0.05689 | 0.05 |
| 1 | 42 | 0.1942 | 0.02845 | 0.05 |
| 1 | 37.8 | 0.17941 | 0.02845 | 0.05 |
| 1 | 33.6 | 0.16465 | 0.02845 | 0.05 |
| 1 | 29.4 | 0.14993 | 0.02845 | 0.05 |
| 1 | 25.2 | 0.13525 | 0.02845 | 0.05 |
| 1 | 21 | 0.12064 | 0.02845 | 0.05 |
| 1 | 16.8 | 0.10612 | 0.02845 | 0.05 |
| 1 | 12.6 | 0.09168 | 0.02845 | 0.05 |
| 1 | 8.4 | 0.0773 | 0.02845 | 0.05 |
| 1 | 4.2 | 0.06286 | 0.02845 | 0.05 |
# -----------------------------------------------------------------------------
# # plot ----------------------- myheight <- 7 mywidth <- 7 mypower$n <-
# mypower$v + 2 mypower$n <- factor(mypower$n, levels =c(myv+2, (myv*0.9)+2,
# (myv*0.8)+2, (myv*0.7)+2, (myv*0.6)+2, (myv*0.5)+2, (myv*0.4)+2,
# (myv*0.3)+2, (myv*0.2)+2, (myv*0.1)+2)) mypower$v <- factor(mypower$v,
# levels =c(myv, myv*0.9, myv*0.8, myv*0.7, myv*0.6, myv*0.5, myv*0.4,
# myv*0.3, myv*0.2, myv*0.1)) p<-ggplot(mypower, aes(x=f2, y=power,
# group=n)) + geom_vline(xintercept = myeffectsize, linetype = 'dashed',
# color = 'darkgray')+ geom_line(aes(color=n))+ geom_point(aes(color=n))+
# geom_point(x = myeffectsize, y = mypowersize, pch = 1, size = 5, color =
# 'darkgray')+ theme_classic()+ labs(tag ='B')+ labs(color = 'n (total
# barcodes)')+ scale_x_continuous(name = '', breaks = c(myeffectsize*0.05,
# myeffectsize*0.1, myeffectsize*0.25, myeffectsize*0.5, myeffectsize*0.75,
# myeffectsize, myeffectsize*1.25, myeffectsize*1.5, myeffectsize*1.75,
# myeffectsize*2), labels = c('0.05%','', '0.25%', '0.5%', '0.75%', '1.0%',
# '1.25%', '1.5%', '1.75%', '2.0%'))+ theme(legend.position = 'none')+
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) p pB <- p
# 250gen DATA
# -----------------------------------------------------------------------------------------
# individual BC
# ---------------------------------------------------------------- power to
# detect fitness increase / decrease (t-tests)
# -------------------------------------------- d = m1 - m2 / q; m1 is mean
# group 1, m2 is mean group 2, q is common standard deviation in the two
# groups here m1 is change in fitness and m2 is 0. mean q is used and is the
# weighted mean of all population standard deviation of the four replicate
# sets in each row of the dataset.
# get data and conduct population sd calculation -----------
my <- myfa_w
my$psd_r1 <- (my$deltafit - my$mndeltafit)^2
my$psd_r2 <- (my$deltafit_2 - my$mndeltafit)^2
my$psd_r3 <- (my$deltafit_3 - my$mndeltafit)^2
my$psd_r4 <- (my$deltafit_4 - my$mndeltafit)^2
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnrdeltafit, na.rm = T)
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 22 * 3, ncol = 5)
colnames(mypower) <- c("fitchange", "psd", "n", "power", "sig.level")
mypower <- as.data.frame(mypower)
mypower$fitchange <- c(0.0025, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035,
0.04, 0.045, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.14, 0.16, 0.18,
0.2, 0.22)
mypower$psd <- psd
mypower$n <- c(rep(2, 22), rep(3, 22), rep(4, 22))
mypower$power <- NA
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 22), rep(2, 22), rep(3, 22))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.t.test(d = mypower$fitchange[i]/mypower$psd[i], n = mypower$n[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_9.html")
| fitchange | psd | n | power | sig.level |
|---|---|---|---|---|
| 0.0025 | 0.02512 | 2 | 0.05046 | 0.05 |
| 0.005 | 0.02512 | 2 | 0.05183 | 0.05 |
| 0.01 | 0.02512 | 2 | 0.05731 | 0.05 |
| 0.015 | 0.02512 | 2 | 0.06638 | 0.05 |
| 0.02 | 0.02512 | 2 | 0.07892 | 0.05 |
| 0.025 | 0.02512 | 2 | 0.09479 | 0.05 |
| 0.03 | 0.02512 | 2 | 0.11383 | 0.05 |
| 0.035 | 0.02512 | 2 | 0.13581 | 0.05 |
| 0.04 | 0.02512 | 2 | 0.16049 | 0.05 |
| 0.045 | 0.02512 | 2 | 0.18762 | 0.05 |
| 0.05 | 0.02512 | 2 | 0.2169 | 0.05 |
| 0.06 | 0.02512 | 2 | 0.28072 | 0.05 |
| 0.07 | 0.02512 | 2 | 0.34947 | 0.05 |
| 0.08 | 0.02512 | 2 | 0.42067 | 0.05 |
| 0.09 | 0.02512 | 2 | 0.49199 | 0.05 |
| 0.1 | 0.02512 | 2 | 0.56137 | 0.05 |
| 0.12 | 0.02512 | 2 | 0.6878 | 0.05 |
| 0.14 | 0.02512 | 2 | 0.79112 | 0.05 |
| 0.16 | 0.02512 | 2 | 0.86862 | 0.05 |
| 0.18 | 0.02512 | 2 | 0.92232 | 0.05 |
| 0.2 | 0.02512 | 2 | 0.95683 | 0.05 |
| 0.22 | 0.02512 | 2 | 0.97744 | 0.05 |
| 0.0025 | 0.02512 | 3 | 0.05106 | 0.05 |
| 0.005 | 0.02512 | 3 | 0.05423 | 0.05 |
| 0.01 | 0.02512 | 3 | 0.06698 | 0.05 |
| 0.015 | 0.02512 | 3 | 0.08841 | 0.05 |
| 0.02 | 0.02512 | 3 | 0.11868 | 0.05 |
| 0.025 | 0.02512 | 3 | 0.15778 | 0.05 |
| 0.03 | 0.02512 | 3 | 0.20539 | 0.05 |
| 0.035 | 0.02512 | 3 | 0.26078 | 0.05 |
| 0.04 | 0.02512 | 3 | 0.32273 | 0.05 |
| 0.045 | 0.02512 | 3 | 0.3896 | 0.05 |
| 0.05 | 0.02512 | 3 | 0.45936 | 0.05 |
| 0.06 | 0.02512 | 3 | 0.59886 | 0.05 |
| 0.07 | 0.02512 | 3 | 0.72487 | 0.05 |
| 0.08 | 0.02512 | 3 | 0.82621 | 0.05 |
| 0.09 | 0.02512 | 3 | 0.89913 | 0.05 |
| 0.1 | 0.02512 | 3 | 0.94628 | 0.05 |
| 0.12 | 0.02512 | 3 | 0.98825 | 0.05 |
| 0.14 | 0.02512 | 3 | 0.99819 | 0.05 |
| 0.16 | 0.02512 | 3 | 0.9998 | 0.05 |
| 0.18 | 0.02512 | 3 | 0.99998 | 0.05 |
| 0.2 | 0.02512 | 3 | 1 | 0.05 |
| 0.22 | 0.02512 | 3 | 1 | 0.05 |
| 0.0025 | 0.02512 | 4 | 0.05165 | 0.05 |
| 0.005 | 0.02512 | 4 | 0.05661 | 0.05 |
| 0.01 | 0.02512 | 4 | 0.07666 | 0.05 |
| 0.015 | 0.02512 | 4 | 0.11068 | 0.05 |
| 0.02 | 0.02512 | 4 | 0.15908 | 0.05 |
| 0.025 | 0.02512 | 4 | 0.22158 | 0.05 |
| 0.03 | 0.02512 | 4 | 0.29675 | 0.05 |
| 0.035 | 0.02512 | 4 | 0.38181 | 0.05 |
| 0.04 | 0.02512 | 4 | 0.47272 | 0.05 |
| 0.045 | 0.02512 | 4 | 0.56471 | 0.05 |
| 0.05 | 0.02512 | 4 | 0.65294 | 0.05 |
| 0.06 | 0.02512 | 4 | 0.8026 | 0.05 |
| 0.07 | 0.02512 | 4 | 0.90401 | 0.05 |
| 0.08 | 0.02512 | 4 | 0.96036 | 0.05 |
| 0.09 | 0.02512 | 4 | 0.98615 | 0.05 |
| 0.1 | 0.02512 | 4 | 0.99592 | 0.05 |
| 0.12 | 0.02512 | 4 | 0.99979 | 0.05 |
| 0.14 | 0.02512 | 4 | 0.99999 | 0.05 |
| 0.16 | 0.02512 | 4 | 1 | 0.05 |
| 0.18 | 0.02512 | 4 | 1 | 0.05 |
| 0.2 | 0.02512 | 4 | 1 | 0.05 |
| 0.22 | 0.02512 | 4 | 1 | 0.05 |
# -----------------------------------------------------------------------------
# # Plotting ------------------------------------------------- # plot effect
# size x power ------------- mypower$n <- factor(mypower$n, levels =
# c(4,3,2)) p<-ggplot(mypower, aes(x=fitchange, y=power, group=n)) +
# geom_vline(xintercept = 0.01, linetype = 'dashed', color = 'gray')+
# geom_vline(xintercept = 0.025, linetype = 'dashed', color = 'gray')+
# geom_vline(xintercept = 0.05, linetype = 'dashed', color = 'gray')+
# geom_vline(xintercept = 0.1, linetype = 'dashed', color = 'gray')+
# geom_line(aes(color=n))+ geom_point(aes(color=n))+ geom_point(x = 0.01, y
# = pwr.t.test(d = 0.01 / psd, n = 4, sig.level = 0.05)$power, pch = 1, size
# = 5, color = 'gray')+ geom_point(x = 0.025, y = pwr.t.test(d = 0.025 /
# psd, n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = 'gray')+
# geom_point(x = 0.05, y = pwr.t.test(d = 0.05 / psd, n = 4, sig.level =
# 0.05)$power, pch = 1, size = 5, color = 'gray')+ geom_point(x = 0.1, y =
# pwr.t.test(d = 0.1 / psd, n = 4, sig.level = 0.05)$power, pch = 1, size =
# 5, color = 'gray')+ scale_x_continuous(name = 'Fitness Change', breaks =
# c(0.01, 0.025, 0.05, 0.1), labels = c('1%', '2.5%', '5%', '10%'), limits =
# c(0,0.1))+ labs(color = 'n (replicates \n per timepoint)')+
# theme_classic()+ labs(tag ='C')+ theme(legend.direction = 'horizontal')+
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) legend1 <-
# get_legend(p) p <- p + theme(legend.position='none') pC <- p
# treatment
# -------------------------------------------------------------------- run
# the appropriate model (fit equivalence model from above -- telling us
# ability to measure fit for each bcid based on our 10 replicates.)
my <- myfa[order(myfa$bctID), ]
mymod <- lm(my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
summary(mymod)
##
## Call:
## lm(formula = my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## my$bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## my$bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## my$bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## my$bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## my$bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## my$bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## my$bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## my$bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## my$bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## my$bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## my$bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## my$bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## my$bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## my$bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## my$bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## my$bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## my$bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## my$bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## my$bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## my$bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## my$bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## my$bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## my$bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## my$bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## my$bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## my$bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## my$bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## my$bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## my$bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## my$bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## my$bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## my$bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## my$bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## my$bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## my$bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## my$bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## my$bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## my$bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## my$bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## my$bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## my$bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## my$bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## my$bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## my$bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## my$bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## my$bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## my$bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## my$bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## my$bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## my$bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## my$bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## my$bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## my$bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## my$bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## my$bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## my$bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## my$bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## my$bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## my$bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## my$bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## my$bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## my$bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## my$bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## my$bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## my$bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## my$bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## my$bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## my$bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## my$bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## my$bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## my$bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## my$bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## my$bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## my$bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## my$bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## my$bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## my$bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## my$bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## my$bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## my$bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## my$bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## my$bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## my$bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## my$bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## my$bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## my$bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## my$bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## my$bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## my$bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## my$bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## my$bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## my$bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## my$bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## my$bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## my$bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## my$bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## my$bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## my$bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## my$bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## my$bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## my$bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## my$bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## my$bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## my$bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## my$bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## my$bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## my$bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## my$bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## my$bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## my$bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## my$bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## my$bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## my$bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## my$bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## my$bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## my$bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## my$bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## my$bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## my$bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## my$bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## my$bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## my$bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## my$bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## my$bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## my$bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## my$bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## my$bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## my$bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## my$bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## my$bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## my$bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## my$bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## my$bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## my$bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## my$bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## my$bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## my$bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## my$bctIDCM+Saltdiploid0.001d1A3 0.153770
## my$bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## my$bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## my$bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## my$bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## my$bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## my$bctIDCM+Saltdiploid0.001d1F5 0.208173
## my$bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## my$bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## my$bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## my$bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## my$bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## my$bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## my$bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## my$bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## my$bctIDCM+Saltdiploid0.001d1H2 0.747562
## my$bctIDCM+Saltdiploid0.001d1H3 0.191568
## my$bctIDCM+Saltdiploid0.001d1H4 0.269265
## my$bctIDCM+Saltdiploid0.001d1H5 0.767557
## my$bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## my$bctIDCMdiploid0.00025d1A2 0.329882
## my$bctIDCMdiploid0.00025d1A3 0.557181
## my$bctIDCMdiploid0.00025d1A4 0.903664
## my$bctIDCMdiploid0.00025d1A5 0.077226 .
## my$bctIDCMdiploid0.00025d1A6 0.358086
## my$bctIDCMdiploid0.00025d1A7 0.786112
## my$bctIDCMdiploid0.00025d1B10 0.624463
## my$bctIDCMdiploid0.00025d1B11 0.166007
## my$bctIDCMdiploid0.00025d1B12 0.103764
## my$bctIDCMdiploid0.00025d1B9 0.107440
## my$bctIDCMdiploid0.00025d1C10 0.368373
## my$bctIDCMdiploid0.00025d1C11 0.048000 *
## my$bctIDCMdiploid0.00025d1C12 0.259774
## my$bctIDCMdiploid0.00025d1C9 0.662997
## my$bctIDCMdiploid0.00025d1D2 0.946757
## my$bctIDCMdiploid0.00025d1E2 0.221035
## my$bctIDCMdiploid0.00025d1H2 0.666246
## my$bctIDCMdiploid0.00025d1H3 0.792714
## my$bctIDCMdiploid0.00025d1H4 0.447657
## my$bctIDCMdiploid0.00025d1H5 0.065659 .
## my$bctIDCMdiploid0.00025d1H6 0.759244
## my$bctIDCMdiploid0.00025d1H7 0.945372
## my$bctIDCMdiploid0.001d1A11 0.047218 *
## my$bctIDCMdiploid0.001d1A2 0.568387
## my$bctIDCMdiploid0.001d1A2B 0.445453
## my$bctIDCMdiploid0.001d1A3 0.982727
## my$bctIDCMdiploid0.001d1A3B 0.774047
## my$bctIDCMdiploid0.001d1A4 0.174949
## my$bctIDCMdiploid0.001d1A4B 0.908856
## my$bctIDCMdiploid0.001d1A5 0.061817 .
## my$bctIDCMdiploid0.001d1A5B 0.726909
## my$bctIDCMdiploid0.001d1A6 0.074694 .
## my$bctIDCMdiploid0.001d1A6B 0.476265
## my$bctIDCMdiploid0.001d1A7 0.001518 **
## my$bctIDCMdiploid0.001d1A8 0.196716
## my$bctIDCMdiploid0.001d1A9 0.044951 *
## my$bctIDCMdiploid0.001d1B1 0.006576 **
## my$bctIDCMdiploid0.001d1B2 0.003039 **
## my$bctIDCMdiploid0.001d1C1 0.002576 **
## my$bctIDCMdiploid0.001d1C2 0.013632 *
## my$bctIDCMdiploid0.001d1D4 0.096546 .
## my$bctIDCMdiploid0.001d1D5 0.152415
## my$bctIDCMdiploid0.001d1D6 0.315226
## my$bctIDCMdiploid0.001d1D7 0.310496
## my$bctIDCMdiploid0.001d1D8 0.240516
## my$bctIDCMdiploid0.001d1E4 0.700111
## my$bctIDCMdiploid0.001d1E5 0.197395
## my$bctIDCMdiploid0.001d1E6 0.023170 *
## my$bctIDCMdiploid0.001d1E7 0.008807 **
## my$bctIDCMdiploid0.001d1E8 0.690775
## my$bctIDCMdiploid0.001d1H11 0.038086 *
## my$bctIDCMdiploid0.001d1H2 0.502150
## my$bctIDCMdiploid0.001d1H2B 0.304231
## my$bctIDCMdiploid0.001d1H3 0.767194
## my$bctIDCMdiploid0.001d1H3B 0.998072
## my$bctIDCMdiploid0.001d1H4 0.721741
## my$bctIDCMdiploid0.001d1H4B 0.915822
## my$bctIDCMdiploid0.001d1H5 0.374428
## my$bctIDCMdiploid0.001d1H5B 0.839864
## my$bctIDCMdiploid0.001d1H6 0.011740 *
## my$bctIDCMdiploid0.001d1H6B 0.055013 .
## my$bctIDCMdiploid0.001d1H7 0.722004
## my$bctIDCMdiploid0.001d1H8 0.534803
## my$bctIDCMdiploid0.001d1H9 0.407275
## my$bctIDCMdiploid0.004d1A2 0.965323
## my$bctIDCMdiploid0.004d1A3 0.522127
## my$bctIDCMdiploid0.004d1A4 0.003677 **
## my$bctIDCMdiploid0.004d1A5 0.004378 **
## my$bctIDCMdiploid0.004d1A6 0.262755
## my$bctIDCMdiploid0.004d1A7 0.153420
## my$bctIDCMdiploid0.004d1B3 0.089106 .
## my$bctIDCMdiploid0.004d1B4 0.060315 .
## my$bctIDCMdiploid0.004d1B5 0.001775 **
## my$bctIDCMdiploid0.004d1B6 0.002307 **
## my$bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## my$bctIDCMdiploid0.004d1C3 0.003811 **
## my$bctIDCMdiploid0.004d1C4 0.096605 .
## my$bctIDCMdiploid0.004d1C5 0.000868 ***
## my$bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## my$bctIDCMdiploid0.004d1C7 0.186138
## my$bctIDCMdiploid0.004d1H2 0.335547
## my$bctIDCMdiploid0.004d1H3 0.333005
## my$bctIDCMdiploid0.004d1H4 0.857174
## my$bctIDCMdiploid0.004d1H5 0.001720 **
## my$bctIDCMdiploid0.004d1H6 0.023234 *
## my$bctIDCMdiploid0.004d1H7 0.712242
## my$bctIDCMhaploid0.001d1A2 0.337921
## my$bctIDCMhaploid0.001d1A3 0.034829 *
## my$bctIDCMhaploid0.001d1A3B 0.020091 *
## my$bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## my$bctIDCMhaploid0.001d1A4B 0.504764
## my$bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## my$bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## my$bctIDCMhaploid0.001d1A6 0.005879 **
## my$bctIDCMhaploid0.001d1A6B 0.002633 **
## my$bctIDCMhaploid0.001d1A7 0.216581
## my$bctIDCMhaploid0.001d1A7B 0.177968
## my$bctIDCMhaploid0.001d1H2 0.000139 ***
## my$bctIDCMhaploid0.001d1H3 0.726659
## my$bctIDCMhaploid0.001d1H3B 0.219285
## my$bctIDCMhaploid0.001d1H4 0.090830 .
## my$bctIDCMhaploid0.001d1H4B 0.465230
## my$bctIDCMhaploid0.001d1H5 0.236513
## my$bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## my$bctIDCMhaploid0.001d1H6 0.000766 ***
## my$bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## my$bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## my$bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# power to detect treatment effects ------------------------------
myv <- (22 * 2) - 1 - 1
mypowersize <- (pwr.f2.test(u = 1, v = myv, f2 = myeffectsize, sig.level = 0.05))$power # powersize for plotting below, where 22 is the number of BCs in any given treatment.
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 100, ncol = 5)
colnames(mypower) <- c("u", "v", "power", "f2", "sig.level")
mypower <- as.data.frame(mypower)
mypower$u <- 1
mypower$v <- c(myv, myv * 0.9, myv * 0.8, myv * 0.7, myv * 0.6, myv * 0.5, myv *
0.4, myv * 0.3, myv * 0.2, myv * 0.1)
mypower$power <- NA
mypower$f2 <- c(rep(myeffectsize * 2, 10), rep(myeffectsize * 1.75, 10), rep(myeffectsize *
1.5, 10), rep(myeffectsize * 1.25, 10), rep(myeffectsize * 1, 10), rep(myeffectsize *
0.75, 10), rep(myeffectsize * 0.5, 10), rep(myeffectsize * 0.25, 10), rep(myeffectsize *
0.1, 10), rep(myeffectsize * 0.05, 10))
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10), rep(5, 10),
rep(6, 10), rep(7, 10), rep(8, 10), rep(9, 10), rep(10, 10))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.f2.test(u = mypower$u[i], v = mypower$v[i], f2 = mypower$f2[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
# model output
# -----------------------------------------------------------------
mypower2 <- round(mypower[, 1:5], digits = 5)
tab_df(mypower2, file = "000_Additional_File_10.html")
| u | v | power | f2 | sig.level |
|---|---|---|---|---|
| 1 | 42 | 0.99294 | 0.46428 | 0.05 |
| 1 | 37.8 | 0.98703 | 0.46428 | 0.05 |
| 1 | 33.6 | 0.97653 | 0.46428 | 0.05 |
| 1 | 29.4 | 0.95825 | 0.46428 | 0.05 |
| 1 | 25.2 | 0.92721 | 0.46428 | 0.05 |
| 1 | 21 | 0.876 | 0.46428 | 0.05 |
| 1 | 16.8 | 0.79451 | 0.46428 | 0.05 |
| 1 | 12.6 | 0.67074 | 0.46428 | 0.05 |
| 1 | 8.4 | 0.49405 | 0.46428 | 0.05 |
| 1 | 4.2 | 0.26374 | 0.46428 | 0.05 |
| 1 | 42 | 0.98497 | 0.40625 | 0.05 |
| 1 | 37.8 | 0.97482 | 0.40625 | 0.05 |
| 1 | 33.6 | 0.95838 | 0.40625 | 0.05 |
| 1 | 29.4 | 0.93224 | 0.40625 | 0.05 |
| 1 | 25.2 | 0.8916 | 0.40625 | 0.05 |
| 1 | 21 | 0.83007 | 0.40625 | 0.05 |
| 1 | 16.8 | 0.7399 | 0.40625 | 0.05 |
| 1 | 12.6 | 0.61317 | 0.40625 | 0.05 |
| 1 | 8.4 | 0.44459 | 0.40625 | 0.05 |
| 1 | 4.2 | 0.23739 | 0.40625 | 0.05 |
| 1 | 42 | 0.96879 | 0.34821 | 0.05 |
| 1 | 37.8 | 0.95221 | 0.34821 | 0.05 |
| 1 | 33.6 | 0.92766 | 0.34821 | 0.05 |
| 1 | 29.4 | 0.89194 | 0.34821 | 0.05 |
| 1 | 25.2 | 0.84097 | 0.34821 | 0.05 |
| 1 | 21 | 0.76995 | 0.34821 | 0.05 |
| 1 | 16.8 | 0.67383 | 0.34821 | 0.05 |
| 1 | 12.6 | 0.5484 | 0.34821 | 0.05 |
| 1 | 8.4 | 0.39238 | 0.34821 | 0.05 |
| 1 | 4.2 | 0.21076 | 0.34821 | 0.05 |
| 1 | 42 | 0.93704 | 0.29018 | 0.05 |
| 1 | 37.8 | 0.91164 | 0.29018 | 0.05 |
| 1 | 33.6 | 0.87718 | 0.29018 | 0.05 |
| 1 | 29.4 | 0.83114 | 0.29018 | 0.05 |
| 1 | 25.2 | 0.77067 | 0.29018 | 0.05 |
| 1 | 21 | 0.69287 | 0.29018 | 0.05 |
| 1 | 16.8 | 0.59524 | 0.29018 | 0.05 |
| 1 | 12.6 | 0.4765 | 0.29018 | 0.05 |
| 1 | 8.4 | 0.33773 | 0.29018 | 0.05 |
| 1 | 4.2 | 0.18392 | 0.29018 | 0.05 |
| 1 | 42 | 0.87741 | 0.23214 | 0.05 |
| 1 | 37.8 | 0.84173 | 0.23214 | 0.05 |
| 1 | 33.6 | 0.79723 | 0.23214 | 0.05 |
| 1 | 29.4 | 0.74243 | 0.23214 | 0.05 |
| 1 | 25.2 | 0.6759 | 0.23214 | 0.05 |
| 1 | 21 | 0.59651 | 0.23214 | 0.05 |
| 1 | 16.8 | 0.50371 | 0.23214 | 0.05 |
| 1 | 12.6 | 0.39793 | 0.23214 | 0.05 |
| 1 | 8.4 | 0.28108 | 0.23214 | 0.05 |
| 1 | 4.2 | 0.15695 | 0.23214 | 0.05 |
| 1 | 42 | 0.77158 | 0.17411 | 0.05 |
| 1 | 37.8 | 0.72743 | 0.17411 | 0.05 |
| 1 | 33.6 | 0.67653 | 0.17411 | 0.05 |
| 1 | 29.4 | 0.61844 | 0.17411 | 0.05 |
| 1 | 25.2 | 0.55288 | 0.17411 | 0.05 |
| 1 | 21 | 0.47987 | 0.17411 | 0.05 |
| 1 | 16.8 | 0.39983 | 0.17411 | 0.05 |
| 1 | 12.6 | 0.3137 | 0.17411 | 0.05 |
| 1 | 8.4 | 0.22305 | 0.17411 | 0.05 |
| 1 | 4.2 | 0.12996 | 0.17411 | 0.05 |
| 1 | 42 | 0.59797 | 0.11607 | 0.05 |
| 1 | 37.8 | 0.55355 | 0.11607 | 0.05 |
| 1 | 33.6 | 0.50585 | 0.11607 | 0.05 |
| 1 | 29.4 | 0.45496 | 0.11607 | 0.05 |
| 1 | 25.2 | 0.40109 | 0.11607 | 0.05 |
| 1 | 21 | 0.34458 | 0.11607 | 0.05 |
| 1 | 16.8 | 0.28588 | 0.11607 | 0.05 |
| 1 | 12.6 | 0.2256 | 0.11607 | 0.05 |
| 1 | 8.4 | 0.16446 | 0.11607 | 0.05 |
| 1 | 4.2 | 0.10305 | 0.11607 | 0.05 |
| 1 | 42 | 0.34535 | 0.05804 | 0.05 |
| 1 | 37.8 | 0.31637 | 0.05804 | 0.05 |
| 1 | 33.6 | 0.28693 | 0.05804 | 0.05 |
| 1 | 29.4 | 0.25712 | 0.05804 | 0.05 |
| 1 | 25.2 | 0.22705 | 0.05804 | 0.05 |
| 1 | 21 | 0.19683 | 0.05804 | 0.05 |
| 1 | 16.8 | 0.16657 | 0.05804 | 0.05 |
| 1 | 12.6 | 0.13638 | 0.05804 | 0.05 |
| 1 | 8.4 | 0.10634 | 0.05804 | 0.05 |
| 1 | 4.2 | 0.07635 | 0.05804 | 0.05 |
| 1 | 42 | 0.16705 | 0.02321 | 0.05 |
| 1 | 37.8 | 0.15503 | 0.02321 | 0.05 |
| 1 | 33.6 | 0.14305 | 0.02321 | 0.05 |
| 1 | 29.4 | 0.1311 | 0.02321 | 0.05 |
| 1 | 25.2 | 0.11921 | 0.02321 | 0.05 |
| 1 | 21 | 0.10737 | 0.02321 | 0.05 |
| 1 | 16.8 | 0.0956 | 0.02321 | 0.05 |
| 1 | 12.6 | 0.0839 | 0.02321 | 0.05 |
| 1 | 8.4 | 0.07223 | 0.02321 | 0.05 |
| 1 | 4.2 | 0.06049 | 0.02321 | 0.05 |
| 1 | 42 | 0.10748 | 0.01161 | 0.05 |
| 1 | 37.8 | 0.10161 | 0.01161 | 0.05 |
| 1 | 33.6 | 0.09576 | 0.01161 | 0.05 |
| 1 | 29.4 | 0.08993 | 0.01161 | 0.05 |
| 1 | 25.2 | 0.08412 | 0.01161 | 0.05 |
| 1 | 21 | 0.07833 | 0.01161 | 0.05 |
| 1 | 16.8 | 0.07256 | 0.01161 | 0.05 |
| 1 | 12.6 | 0.06681 | 0.01161 | 0.05 |
| 1 | 8.4 | 0.06105 | 0.01161 | 0.05 |
| 1 | 4.2 | 0.05524 | 0.01161 | 0.05 |
# -----------------------------------------------------------------------------
# myheight <- 7 mywidth <- 7 mypower$n <- mypower$v + 2 mypower$n <-
# factor(mypower$n, levels =c(myv+2, (myv*0.9)+2, (myv*0.8)+2, (myv*0.7)+2,
# (myv*0.6)+2, (myv*0.5)+2, (myv*0.4)+2, (myv*0.3)+2, (myv*0.2)+2,
# (myv*0.1)+2)) mypower$v <- factor(mypower$v, levels =c(myv, myv*0.9,
# myv*0.8, myv*0.7, myv*0.6, myv*0.5, myv*0.4, myv*0.3, myv*0.2, myv*0.1))
# p<-ggplot(mypower, aes(x=f2, y=power, group=n)) + geom_vline(xintercept =
# myeffectsize, linetype = 'dashed', color = 'darkgray')+
# geom_line(aes(color=n))+ geom_point(aes(color=n))+ geom_point(x =
# myeffectsize, y = mypowersize, pch = 1, size = 5, color = 'darkgray')+
# theme_classic()+ labs(tag = 'D')+ labs(color = 'n (total barcodes \n in
# two treatments))')+ scale_x_continuous(name = 'Fitness Difference between
# Treatments', breaks = c(myeffectsize*0.05, myeffectsize*0.1,
# myeffectsize*0.25, myeffectsize*0.5, myeffectsize*0.75, myeffectsize,
# myeffectsize*1.25, myeffectsize*1.5, myeffectsize*1.75, myeffectsize*2),
# labels = c('0.05%','', '0.25%', '0.5%', '0.75%', '1.0%', '1.25%', '1.5%',
# '1.75%', '2.0%'))+ theme(legend.direction = 'horizontal')+
# theme(axis.text.x = element_text(angle = 45, hjust = 1)) legend2 <-
# get_legend(p) p <- p + theme(legend.position='none') p pD <- p # Plot
# using grid and output ---------------------------------------------------
# mywidth <- 6.69 myheight <- 6.69 pdf(file = '000_Figure_3.pdf', width =
# mywidth, height = myheight) grid.arrange( grid::textGrob('POC Fitness \n
# Assays', gp =grid::gpar(fontsize = 12, fontface = 'bold'), rot = 90),
# grid::textGrob('250-Generation \n Experiment', gp =grid::gpar(fontsize =
# 12, fontface = 'bold'), rot = 90), pA,pB, pC,pD, legend1, legend2,
# layout_matrix = rbind(c(1,3,3,3,3,4,4,4,4,4,4), c(1,3,3,3,3,4,4,4,4,4,4),
# c(1,3,3,3,3,4,4,4,4,4,4), c(1,3,3,3,3,4,4,4,4,4,4),
# c(2,5,5,5,5,6,6,6,6,6,6), c(2,5,5,5,5,6,6,6,6,6,6),
# c(2,5,5,5,5,6,6,6,6,6,6), c(2,5,5,5,5,6,6,6,6,6,6),
# c(7,7,7,7,7,7,7,7,7,7,7), c(8,8,8,8,8,8,8,8,8,8,8))) dev.off() # one
# column wide, equal height
# ------------------------------------------------------------------------------
rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7, my8, my9, my10,
mymod, mypower, mypower2, p, pA, pB, pC, pD, pow, i, myeffectsize, myheight,
mypowersize, myrmse, myv, mywidth, psd)
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'legend1' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'legend2' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'p' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'pA' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'pB' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'pC' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'pD' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'myheight' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'mywidth' not found
Analysis: Power (Vizualize for Methods manuscript)
# individual bc
# ------------------------------------------------------------------------------------
# # POC DATA
# -------------------------------------------------------------------
my <- poc92 # conduct necessary calculations
my1 <- my[my$exp == 1, ]
colnames(my1) <- paste0(colnames(my1), "_R1")
my2 <- my[my$exp == 2, ]
colnames(my2) <- paste0(colnames(my2), "_R2")
my3 <- my[my$exp == 3, ]
colnames(my3) <- paste0(colnames(my3), "_R3")
my4 <- my[my$exp == 4, ]
colnames(my4) <- paste0(colnames(my4), "_R4")
my5 <- my[my$exp == 5, ]
colnames(my5) <- paste0(colnames(my5), "_R5")
my6 <- my[my$exp == 6, ]
colnames(my6) <- paste0(colnames(my6), "_R6")
my7 <- my[my$exp == 7, ]
colnames(my7) <- paste0(colnames(my7), "_R7")
my8 <- my[my$exp == 8, ]
colnames(my8) <- paste0(colnames(my8), "_R8")
my9 <- my[my$exp == 9, ]
colnames(my9) <- paste0(colnames(my9), "_R9")
my10 <- my[my$exp == 10, ]
colnames(my10) <- paste0(colnames(my10), "_R10")
poc92_w <- cbind(my1, my2, my3, my4, my5, my6, my7, my8, my9, my10)
for (i in 1:nrow(poc92_w)) {
poc92_w$mnrw[i] <- weighted.mean(poc92_w[i, c("rw_R1", "rw_R2", "rw_R3",
"rw_R4", "rw_R5", "rw_R6", "rw_R7", "rw_R8", "rw_R9", "rw_R10")], poc92_w[i,
c("r_R1", "r_R2", "r_R3", "r_R4", "r_R5", "r_R6", "r_R7", "r_R8", "r_R9",
"r_R10")], na.rm = T)
poc92_w$mnr[i] <- rowMeans(poc92_w[i, c("r_R1", "r_R2", "r_R3", "r_R4",
"r_R5", "r_R6", "r_R7", "r_R8", "r_R9", "r_R10")], na.rm = T)
}
my <- poc92_w
my$psd_r1 <- ((my$rw_R1 - my$mnrw)^2)/10
my$psd_r2 <- ((my$rw_R2 - my$mnrw)^2)/10
my$psd_r3 <- ((my$rw_R3 - my$mnrw)^2)/10
my$psd_r4 <- ((my$rw_R4 - my$mnrw)^2)/10
my$psd_r5 <- ((my$rw_R5 - my$mnrw)^2)/10
my$psd_r6 <- ((my$rw_R6 - my$mnrw)^2)/10
my$psd_r7 <- ((my$rw_R7 - my$mnrw)^2)/10
my$psd_r8 <- ((my$rw_R8 - my$mnrw)^2)/10
my$psd_r9 <- ((my$rw_R9 - my$mnrw)^2)/10
my$psd_r10 <- ((my$rw_R10 - my$mnrw)^2)/10
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4, my$psd_r5,
my$psd_r6, my$psd_r7, my$psd_r8, my$psd_r9, my$psd_r10), na.rm = T)
my$psd <- sqrt(my$psd_m) # get the pop standard dev for each entry
psd <- weighted.mean(my$psd, my$mnr, na.rm = T) # get the weighted mean pop standard dev for reporting
psdpoc <- psd
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 22 * 3, ncol = 5)
colnames(mypower) <- c("fitchange", "psd", "n", "power", "sig.level")
mypower <- as.data.frame(mypower)
mypower$fitchange <- c(0.0025, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035,
0.04, 0.045, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.14, 0.16, 0.18,
0.2, 0.22)
mypower$psd <- psd
mypower$n <- c(rep(2, 22), rep(3, 22), rep(4, 22))
mypower$power <- NA
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 22), rep(2, 22), rep(3, 22))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.t.test(d = mypower$fitchange[i]/mypower$psd[i], n = mypower$n[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
mypower$n <- factor(mypower$n, levels = c(4, 3, 2))
mypower <- mypower[mypower$n == 4, ]
mypower_a_poc <- mypower
mypower_a_poc$exp <- "POC Fitness Assays"
# # 250 gen DATA
# ------------------------------------------------------------------- power
# to detect fitness increase / decrease (t-tests)
# -------------------------------------------- d = m1 - m2 / q; m1 is mean
# group 1, m2 is mean group 2, q is common standard deviation in the two
# groups here m1 is change in fitness and m2 is 0. mean q is used and is the
# weighted mean of all population standard deviation of the four replicate
# sets in each row of the dataset.
# get data and conduct population sd calculation -----------
my <- myfa_w
my$psd_r1 <- (my$deltafit - my$mndeltafit)^2
my$psd_r2 <- (my$deltafit_2 - my$mndeltafit)^2
my$psd_r3 <- (my$deltafit_3 - my$mndeltafit)^2
my$psd_r4 <- (my$deltafit_4 - my$mndeltafit)^2
my$psd_m <- rowMeans(cbind(my$psd_r1, my$psd_r2, my$psd_r3, my$psd_r4), na.rm = T)
my$psd <- sqrt(my$psd_m)
psd <- weighted.mean(my$psd, my$mnrdeltafit, na.rm = T)
psd250 <- psd
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 22 * 3, ncol = 5)
colnames(mypower) <- c("fitchange", "psd", "n", "power", "sig.level")
mypower <- as.data.frame(mypower)
mypower$fitchange <- c(0.0025, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035,
0.04, 0.045, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.14, 0.16, 0.18,
0.2, 0.22)
mypower$psd <- psd
mypower$n <- c(rep(2, 22), rep(3, 22), rep(4, 22))
mypower$power <- NA
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 22), rep(2, 22), rep(3, 22))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.t.test(d = mypower$fitchange[i]/mypower$psd[i], n = mypower$n[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
mypower$n <- factor(mypower$n, levels = c(4, 3, 2))
mypower <- mypower[mypower$n == 4, ]
mypower_a_250 <- mypower
mypower_a_250$exp <- "250 Generation Evolution Fitness Assays"
mypower <- rbind(mypower_a_poc, mypower_a_250)
mypower$exp <- as.factor(mypower$exp)
mypower$exp <- relevel(mypower$exp, ref = "POC Fitness Assays")
# Plotting ------------------------------------------------- plot effect
# size x power -------------
p <- ggplot(mypower, aes(x = fitchange, y = power, group = exp)) + geom_vline(xintercept = 0.01,
linetype = "dashed", color = "gray") + geom_vline(xintercept = 0.025, linetype = "dashed",
color = "gray") + geom_vline(xintercept = 0.05, linetype = "dashed", color = "gray") +
geom_vline(xintercept = 0.1, linetype = "dashed", color = "gray") + geom_line(aes(color = exp)) +
geom_point(aes(color = exp)) + geom_point(x = 0.01, y = pwr.t.test(d = 0.01/psdpoc,
n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.025,
y = pwr.t.test(d = 0.025/psdpoc, n = 4, sig.level = 0.05)$power, pch = 1,
size = 5, color = "gray") + geom_point(x = 0.05, y = pwr.t.test(d = 0.05/psdpoc,
n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.1,
y = pwr.t.test(d = 0.1/psdpoc, n = 4, sig.level = 0.05)$power, pch = 1,
size = 5, color = "gray") + geom_point(x = 0.01, y = pwr.t.test(d = 0.01/psd250,
n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.025,
y = pwr.t.test(d = 0.025/psd250, n = 4, sig.level = 0.05)$power, pch = 1,
size = 5, color = "gray") + geom_point(x = 0.05, y = pwr.t.test(d = 0.05/psd250,
n = 4, sig.level = 0.05)$power, pch = 1, size = 5, color = "gray") + geom_point(x = 0.1,
y = pwr.t.test(d = 0.1/psd250, n = 4, sig.level = 0.05)$power, pch = 1,
size = 5, color = "gray") + scale_x_continuous(name = "Fitness difference (& fitness change) \n for individual barcodes \n ",
breaks = c(0.01, 0.025, 0.05, 0.1), labels = c("1%", "2.5%", "5%", "10%"),
limits = c(0, 0.1)) + scale_color_manual(values = setpalette) + labs(color = "Experiment") +
theme_classic() + labs(tag = "A") + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
legend1 <- get_legend(p)
## Warning: Removed 12 rows containing missing values (geom_path).
## Warning: Removed 12 rows containing missing values (geom_point).
p <- p + theme(legend.position = "none")
pA <- p
pA
## Warning: Removed 12 rows containing missing values (geom_path).
## Warning: Removed 12 rows containing missing values (geom_point).
# treatment
# ----------------------------------------------------------------------------------------
# # POC DATA
# ------------------------------------------------------------------- run
# the appropriate model (fit equivalence model from above -- telling us
# ability to measure fit for each bcid based on our 10 replicates.)
my <- poc92[order(poc92$bcid), ]
my$rw_corrected <- my$rw - weighted.mean(my$rw, my$r)
mymod <- lm(my$rw_corrected ~ my$bcid + 0, weights = my$r)
summary(mymod)
##
## Call:
## lm(formula = my$rw_corrected ~ my$bcid + 0, weights = my$r)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.7350 -0.5858 -0.0109 0.5304 5.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## my$bcidd1A10 0.00132555 0.00371437 0.357 0.721280
## my$bcidd1A11 -0.00762370 0.00466055 -1.636 0.102267
## my$bcidd1A2 0.00811450 0.00396019 2.049 0.040778 *
## my$bcidd1A3 0.00494242 0.00439553 1.124 0.261165
## my$bcidd1A4 0.00795573 0.00448555 1.774 0.076495 .
## my$bcidd1A5 0.00103320 0.00520070 0.199 0.842573
## my$bcidd1A6 -0.00085773 0.00357809 -0.240 0.810610
## my$bcidd1A7 0.00344369 0.00512097 0.672 0.501476
## my$bcidd1A8 0.00583557 0.00414724 1.407 0.159778
## my$bcidd1A9 0.00724382 0.00374750 1.933 0.053584 .
## my$bcidd1B1 -0.00527477 0.00502129 -1.050 0.293807
## my$bcidd1B10 -0.00821202 0.00615734 -1.334 0.182674
## my$bcidd1B11 -0.01330702 0.00511428 -2.602 0.009437 **
## my$bcidd1B12 -0.00741678 0.00553232 -1.341 0.180413
## my$bcidd1B2 0.00289719 0.00431315 0.672 0.501957
## my$bcidd1B3 0.00354456 0.00434513 0.816 0.414878
## my$bcidd1B4 0.00951091 0.00421461 2.257 0.024293 *
## my$bcidd1B5 0.00961576 0.00421606 2.281 0.022819 *
## my$bcidd1B6 0.00134667 0.00501745 0.268 0.788462
## my$bcidd1B7 -0.00398474 0.00435334 -0.915 0.360287
## my$bcidd1B8 0.00386754 0.00435584 0.888 0.374856
## my$bcidd1B9 -0.00197406 0.00517408 -0.382 0.702909
## my$bcidd1C1 -0.00130215 0.00523917 -0.249 0.803778
## my$bcidd1C10 -0.00200878 0.00612990 -0.328 0.743221
## my$bcidd1C11 0.00332553 0.00513862 0.647 0.517707
## my$bcidd1C12 -0.00495638 0.00525931 -0.942 0.346266
## my$bcidd1C2 0.00310014 0.00440966 0.703 0.482235
## my$bcidd1C3 0.00170870 0.00544151 0.314 0.753591
## my$bcidd1C4 0.00064326 0.00445091 0.145 0.885124
## my$bcidd1C5 -0.00293377 0.00470909 -0.623 0.533457
## my$bcidd1C6 -0.00108375 0.00401081 -0.270 0.787069
## my$bcidd1C7 -0.00224296 0.00857907 -0.261 0.793814
## my$bcidd1C8 -0.00815331 0.00535937 -1.521 0.128566
## my$bcidd1C9 0.01983075 0.00598788 3.312 0.000968 ***
## my$bcidd1D1 0.00371456 0.00415556 0.894 0.371651
## my$bcidd1D10 0.01036458 0.00405675 2.555 0.010802 *
## my$bcidd1D11 -0.00004981 0.00380942 -0.013 0.989570
## my$bcidd1D12 0.00301900 0.00386910 0.780 0.435449
## my$bcidd1D2 -0.01098378 0.02236414 -0.491 0.623464
## my$bcidd1D3 -0.00440055 0.00562205 -0.783 0.434011
## my$bcidd1D4 -0.00693858 0.00737022 -0.941 0.346760
## my$bcidd1D5 -0.00326940 0.00563709 -0.580 0.562087
## my$bcidd1D6 -0.00844038 0.00554061 -1.523 0.128053
## my$bcidd1D7 -0.00168313 0.00462568 -0.364 0.716052
## my$bcidd1D8 0.00180750 0.00397535 0.455 0.649462
## my$bcidd1D9 0.00014080 0.00545635 0.026 0.979419
## my$bcidd1E1 -0.01270595 0.00523534 -2.427 0.015441 *
## my$bcidd1E10 -0.00147548 0.00433537 -0.340 0.733691
## my$bcidd1E11 -0.00708010 0.00406068 -1.744 0.081609 .
## my$bcidd1E12 0.01157108 0.00442433 2.615 0.009078 **
## my$bcidd1E2 0.00500682 0.00373187 1.342 0.180085
## my$bcidd1E3 0.00762603 0.00554298 1.376 0.169259
## my$bcidd1E4 0.00591987 0.00384425 1.540 0.123964
## my$bcidd1E5 -0.00657763 0.00498803 -1.319 0.187644
## my$bcidd1E6 -0.00139332 0.00372470 -0.374 0.708444
## my$bcidd1E7 0.00852322 0.00396829 2.148 0.032020 *
## my$bcidd1E8 -0.00369015 0.00443221 -0.833 0.405327
## my$bcidd1E9 0.00639603 0.00436339 1.466 0.143076
## my$bcidd1F1 -0.00012498 0.00381935 -0.033 0.973904
## my$bcidd1F10 0.00393912 0.00423405 0.930 0.352467
## my$bcidd1F11 0.00670256 0.00469224 1.428 0.153547
## my$bcidd1F12 0.00778245 0.00385357 2.020 0.043756 *
## my$bcidd1F2 0.00582870 0.00435141 1.339 0.180780
## my$bcidd1F3 0.00651265 0.00596554 1.092 0.275281
## my$bcidd1F4 -0.02625366 0.00923796 -2.842 0.004596 **
## my$bcidd1F5 -0.04834088 0.00429859 -11.246 < 0.0000000000000002 ***
## my$bcidd1F6 -0.00012930 0.00381509 -0.034 0.972972
## my$bcidd1F7 -0.01483489 0.00492987 -3.009 0.002700 **
## my$bcidd1F8 -0.00220117 0.00605837 -0.363 0.716455
## my$bcidd1F9 0.00029767 0.01166664 0.026 0.979651
## my$bcidd1G1 0.01006985 0.00654635 1.538 0.124376
## my$bcidd1G10 0.00690433 0.00358418 1.926 0.054408 .
## my$bcidd1G11 0.00299309 0.00557059 0.537 0.591205
## my$bcidd1G12 0.00147423 0.00344561 0.428 0.668867
## my$bcidd1G2 0.00615615 0.00569582 1.081 0.280096
## my$bcidd1G3 0.00097634 0.00390830 0.250 0.802795
## my$bcidd1G4 -0.00335673 0.00478773 -0.701 0.483433
## my$bcidd1G5 -0.01510445 0.00537164 -2.812 0.005043 **
## my$bcidd1G6 -0.00650477 0.00473025 -1.375 0.169463
## my$bcidd1G7 -0.00911754 0.00430618 -2.117 0.034534 *
## my$bcidd1G8 -0.00467010 0.00489124 -0.955 0.339967
## my$bcidd1G9 -0.00345997 0.00470652 -0.735 0.462463
## my$bcidd1H11 0.00188567 0.00387909 0.486 0.627018
## my$bcidd1H2 -0.00688077 0.00535513 -1.285 0.199193
## my$bcidd1H3 -0.00074760 0.00640698 -0.117 0.907138
## my$bcidd1H4 -0.03016766 0.00925002 -3.261 0.001155 **
## my$bcidd1H5 -0.00289320 0.00438300 -0.660 0.509377
## my$bcidd1H6 0.00117090 0.00365259 0.321 0.748621
## my$bcidd1H7 -0.00459377 0.00852914 -0.539 0.590312
## my$bcidd1H8 -0.00449698 0.00614172 -0.732 0.464255
## my$bcidd1H9 -0.00798745 0.00584794 -1.366 0.172359
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.127 on 819 degrees of freedom
## Multiple R-squared: 0.2653, Adjusted R-squared: 0.1836
## F-statistic: 3.25 on 91 and 819 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~1.76 fitness change
myeffectsize <- 1/myrmse # effect size of 1 is a 1.76 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
myv <- (22 * 2) - 1 - 1
mypowersize <- (pwr.f2.test(u = 1, v = myv, f2 = myeffectsize, sig.level = 0.05))$power # powersize for plotting below, where 22 is the number of BCs in any given treatment.
# prepare plot data ----------
mypower <- matrix(nrow = 100, ncol = 5)
colnames(mypower) <- c("u", "v", "power", "f2", "sig.level")
mypower <- as.data.frame(mypower)
mypower$u <- 1
mypower$v <- c(myv, myv * 0.9, myv * 0.8, myv * 0.7, myv * 0.6, myv * 0.5, myv *
0.4, myv * 0.3, myv * 0.2, myv * 0.1)
mypower$power <- NA
mypower$f2 <- c(rep(myeffectsize * 2, 10), rep(myeffectsize * 1.75, 10), rep(myeffectsize *
1.5, 10), rep(myeffectsize * 1.25, 10), rep(myeffectsize * 1, 10), rep(myeffectsize *
0.75, 10), rep(myeffectsize * 0.5, 10), rep(myeffectsize * 0.25, 10), rep(myeffectsize *
0.1, 10), rep(myeffectsize * 0.05, 10))
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10), rep(5, 10),
rep(6, 10), rep(7, 10), rep(8, 10), rep(9, 10), rep(10, 10))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.f2.test(u = mypower$u[i], v = mypower$v[i], f2 = mypower$f2[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
mypower$n <- mypower$v + 2
mypower$n <- factor(mypower$n, levels = c(myv + 2, (myv * 0.9) + 2, (myv * 0.8) +
2, (myv * 0.7) + 2, (myv * 0.6) + 2, (myv * 0.5) + 2, (myv * 0.4) + 2, (myv *
0.3) + 2, (myv * 0.2) + 2, (myv * 0.1) + 2))
mypower$v <- factor(mypower$v, levels = c(myv, myv * 0.9, myv * 0.8, myv * 0.7,
myv * 0.6, myv * 0.5, myv * 0.4, myv * 0.3, myv * 0.2, myv * 0.1))
mypower <- mypower[mypower$n == 44, ]
mypower$f2 <- mypower$f2 * myrmse
mypower_b_poc <- mypower
mypower_b_poc$exp <- "POC Fitness Assays"
myeffectsize_b_poc <- myeffectsize * myrmse
mypowersize_b_poc <- mypowersize
# 250gen DATA
# -----------------------------------------------------------------------------------------
# run the appropriate model (fit equivalence model from above -- telling us
# ability to measure fit for each bcid based on our 10 replicates.)
my <- myfa[order(myfa$bctID), ]
mymod <- lm(my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
summary(mymod)
##
## Call:
## lm(formula = my$deltafit ~ my$bctID + 0, weights = my$rdeltafit)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -8.9024 -0.9879 0.0734 0.9561 4.8229
##
## Coefficients:
## Estimate Std. Error t value
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.00224666 0.01223756 0.184
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.01765500 0.01365937 1.293
## my$bctIDCM+Ethanoldiploid0.001d1A5 -0.00183138 0.01601185 -0.114
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.00847721 0.01160185 0.731
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.00837054 0.01577339 0.531
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.03354979 0.01054805 3.181
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.02980938 0.01093362 2.726
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.02399057 0.01018367 2.356
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.02343344 0.02027118 1.156
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.04807661 0.01120179 4.292
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.03162314 0.01125755 2.809
## my$bctIDCM+Ethanoldiploid0.001d1E12 -0.03049231 0.01211023 -2.518
## my$bctIDCM+Ethanoldiploid0.001d1E9 -0.00999714 0.01070592 -0.934
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.00938513 0.00879011 1.068
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.01938389 0.01121524 1.728
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.01657615 0.01500470 1.105
## my$bctIDCM+Ethanoldiploid0.001d1G2 -0.01127658 0.01818702 -0.620
## my$bctIDCM+Ethanoldiploid0.001d1H2 -0.01386016 0.01837215 -0.754
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.00296372 0.02376768 0.125
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.00347374 0.01381229 0.251
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.00333766 0.01279047 0.261
## my$bctIDCM+Ethanoldiploid0.001d1H7 -0.00551949 0.02558074 -0.216
## my$bctIDCM+Saltdiploid0.001d1A2 0.04403600 0.01205900 3.652
## my$bctIDCM+Saltdiploid0.001d1A3 0.01686781 0.01180626 1.429
## my$bctIDCM+Saltdiploid0.001d1A4 0.02820342 0.01240104 2.274
## my$bctIDCM+Saltdiploid0.001d1A5 0.39852910 0.19667177 2.026
## my$bctIDCM+Saltdiploid0.001d1A7 0.02578701 0.01322947 1.949
## my$bctIDCM+Saltdiploid0.001d1F3 0.14079891 0.02033980 6.922
## my$bctIDCM+Saltdiploid0.001d1F4 0.18806044 0.02296431 8.189
## my$bctIDCM+Saltdiploid0.001d1F5 0.19913858 0.15799739 1.260
## my$bctIDCM+Saltdiploid0.001d1F6 0.23451078 0.01480773 15.837
## my$bctIDCM+Saltdiploid0.001d1F7 0.15255139 0.02121079 7.192
## my$bctIDCM+Saltdiploid0.001d1F8 0.12888704 0.01760927 7.319
## my$bctIDCM+Saltdiploid0.001d1G3 0.10101471 0.03918185 2.578
## my$bctIDCM+Saltdiploid0.001d1G4 0.13402619 0.04517263 2.967
## my$bctIDCM+Saltdiploid0.001d1G5 0.09585476 0.01936410 4.950
## my$bctIDCM+Saltdiploid0.001d1G6 0.08971402 0.03285475 2.731
## my$bctIDCM+Saltdiploid0.001d1G7 0.08740043 0.02106254 4.150
## my$bctIDCM+Saltdiploid0.001d1G8 0.12572385 0.03290001 3.821
## my$bctIDCM+Saltdiploid0.001d1H2 0.00495816 0.01539561 0.322
## my$bctIDCM+Saltdiploid0.001d1H3 0.02115633 0.01617586 1.308
## my$bctIDCM+Saltdiploid0.001d1H4 0.02137396 0.01932366 1.106
## my$bctIDCM+Saltdiploid0.001d1H5 0.00354951 0.01200179 0.296
## my$bctIDCM+Saltdiploid0.001d1H7 0.09417670 0.02624046 3.589
## my$bctIDCMdiploid0.00025d1A2 0.01559982 0.01599335 0.975
## my$bctIDCMdiploid0.00025d1A3 -0.00878718 0.01495776 -0.587
## my$bctIDCMdiploid0.00025d1A4 0.00174608 0.01441827 0.121
## my$bctIDCMdiploid0.00025d1A5 0.02677688 0.01511949 1.771
## my$bctIDCMdiploid0.00025d1A6 0.01198957 0.01303287 0.920
## my$bctIDCMdiploid0.00025d1A7 -0.00440271 0.01621492 -0.272
## my$bctIDCMdiploid0.00025d1B10 -0.00809898 0.01653299 -0.490
## my$bctIDCMdiploid0.00025d1B11 0.01723177 0.01242047 1.387
## my$bctIDCMdiploid0.00025d1B12 0.02983743 0.01830367 1.630
## my$bctIDCMdiploid0.00025d1B9 0.02155039 0.01336058 1.613
## my$bctIDCMdiploid0.00025d1C10 -0.01674094 0.01859239 -0.900
## my$bctIDCMdiploid0.00025d1C11 0.03216629 0.01622343 1.983
## my$bctIDCMdiploid0.00025d1C12 0.01496869 0.01326625 1.128
## my$bctIDCMdiploid0.00025d1C9 -0.00518047 0.01188006 -0.436
## my$bctIDCMdiploid0.00025d1D2 0.00370897 0.05550949 0.067
## my$bctIDCMdiploid0.00025d1E2 0.01289359 0.01052145 1.225
## my$bctIDCMdiploid0.00025d1H2 0.00760594 0.01762321 0.432
## my$bctIDCMdiploid0.00025d1H3 -0.00458899 0.01745253 -0.263
## my$bctIDCMdiploid0.00025d1H4 0.01843903 0.02426240 0.760
## my$bctIDCMdiploid0.00025d1H5 0.02541993 0.01377634 1.845
## my$bctIDCMdiploid0.00025d1H6 0.00414724 0.01352400 0.307
## my$bctIDCMdiploid0.00025d1H7 0.00190764 0.02782539 0.069
## my$bctIDCMdiploid0.001d1A11 0.03612225 0.01815435 1.990
## my$bctIDCMdiploid0.001d1A2 0.00809289 0.01417709 0.571
## my$bctIDCMdiploid0.001d1A2B 0.00927271 0.01214216 0.764
## my$bctIDCMdiploid0.001d1A3 0.00032174 0.01485237 0.022
## my$bctIDCMdiploid0.001d1A3B 0.00398055 0.01385717 0.287
## my$bctIDCMdiploid0.001d1A4 0.01780845 0.01310805 1.359
## my$bctIDCMdiploid0.001d1A4B 0.00153330 0.01338588 0.115
## my$bctIDCMdiploid0.001d1A5 0.02937872 0.01569201 1.872
## my$bctIDCMdiploid0.001d1A5B -0.00512256 0.01465871 -0.349
## my$bctIDCMdiploid0.001d1A6 0.02327336 0.01302785 1.786
## my$bctIDCMdiploid0.001d1A6B 0.00955064 0.01339667 0.713
## my$bctIDCMdiploid0.001d1A7 0.04290593 0.01344789 3.191
## my$bctIDCMdiploid0.001d1A8 0.01512575 0.01169949 1.293
## my$bctIDCMdiploid0.001d1A9 0.02268337 0.01128163 2.011
## my$bctIDCMdiploid0.001d1B1 0.05232713 0.01916650 2.730
## my$bctIDCMdiploid0.001d1B2 0.04063255 0.01363624 2.980
## my$bctIDCMdiploid0.001d1C1 0.05496896 0.01813523 3.031
## my$bctIDCMdiploid0.001d1C2 0.03217244 0.01299148 2.476
## my$bctIDCMdiploid0.001d1D4 0.08813997 0.05292837 1.665
## my$bctIDCMdiploid0.001d1D5 -0.01879019 0.01310839 -1.433
## my$bctIDCMdiploid0.001d1D6 0.04266468 0.04243456 1.005
## my$bctIDCMdiploid0.001d1D7 0.01221091 0.01202676 1.015
## my$bctIDCMdiploid0.001d1D8 -0.01270792 0.01081313 -1.175
## my$bctIDCMdiploid0.001d1E4 0.00301728 0.00782870 0.385
## my$bctIDCMdiploid0.001d1E5 0.03661495 0.02836404 1.291
## my$bctIDCMdiploid0.001d1E6 -0.02094510 0.00919326 -2.278
## my$bctIDCMdiploid0.001d1E7 0.02727065 0.01036588 2.631
## my$bctIDCMdiploid0.001d1E8 -0.00435115 0.01093102 -0.398
## my$bctIDCMdiploid0.001d1H11 0.03103102 0.01491899 2.080
## my$bctIDCMdiploid0.001d1H2 0.01207171 0.01797336 0.672
## my$bctIDCMdiploid0.001d1H2B -0.01771525 0.01722331 -1.029
## my$bctIDCMdiploid0.001d1H3 -0.00572467 0.01932551 -0.296
## my$bctIDCMdiploid0.001d1H3B 0.00004411 0.01824231 0.002
## my$bctIDCMdiploid0.001d1H4 0.00887392 0.02490203 0.356
## my$bctIDCMdiploid0.001d1H4B -0.00268735 0.02541084 -0.106
## my$bctIDCMdiploid0.001d1H5 0.01359003 0.01528546 0.889
## my$bctIDCMdiploid0.001d1H5B -0.00274906 0.01359686 -0.202
## my$bctIDCMdiploid0.001d1H6 0.03458717 0.01367055 2.530
## my$bctIDCMdiploid0.001d1H6B 0.02557412 0.01329414 1.924
## my$bctIDCMdiploid0.001d1H7 0.00942620 0.02647801 0.356
## my$bctIDCMdiploid0.001d1H8 0.01372130 0.02208970 0.621
## my$bctIDCMdiploid0.001d1H9 0.01922550 0.02317816 0.829
## my$bctIDCMdiploid0.004d1A2 0.00073845 0.01697648 0.043
## my$bctIDCMdiploid0.004d1A3 0.01219203 0.01903325 0.641
## my$bctIDCMdiploid0.004d1A4 0.05056340 0.01731766 2.920
## my$bctIDCMdiploid0.004d1A5 0.05149485 0.01798080 2.864
## my$bctIDCMdiploid0.004d1A6 0.01931859 0.01722893 1.121
## my$bctIDCMdiploid0.004d1A7 0.02865780 0.02004130 1.430
## my$bctIDCMdiploid0.004d1B3 0.02462782 0.01445493 1.704
## my$bctIDCMdiploid0.004d1B4 0.03125331 0.01659619 1.883
## my$bctIDCMdiploid0.004d1B5 0.04298949 0.01367318 3.144
## my$bctIDCMdiploid0.004d1B6 0.05327518 0.01738231 3.065
## my$bctIDCMdiploid0.004d1B7 0.07749931 0.01526527 5.077
## my$bctIDCMdiploid0.004d1C3 0.04273794 0.01469507 2.908
## my$bctIDCMdiploid0.004d1C4 0.02312320 0.01388803 1.665
## my$bctIDCMdiploid0.004d1C5 0.04334705 0.01293091 3.352
## my$bctIDCMdiploid0.004d1C6 0.06362223 0.01397915 4.551
## my$bctIDCMdiploid0.004d1C7 0.03835768 0.02896918 1.324
## my$bctIDCMdiploid0.004d1H2 0.02225147 0.02308198 0.964
## my$bctIDCMdiploid0.004d1H3 0.02106572 0.02173722 0.969
## my$bctIDCMdiploid0.004d1H4 0.00548235 0.03044485 0.180
## my$bctIDCMdiploid0.004d1H5 0.06026645 0.01911119 3.153
## my$bctIDCMdiploid0.004d1H6 0.04022208 0.01766259 2.277
## my$bctIDCMdiploid0.004d1H7 0.01164662 0.03155608 0.369
## my$bctIDCMhaploid0.001d1A2 0.17748276 0.18501465 0.959
## my$bctIDCMhaploid0.001d1A3 0.03475922 0.01642162 2.117
## my$bctIDCMhaploid0.001d1A3B 0.02653209 0.01137336 2.333
## my$bctIDCMhaploid0.001d1A4 0.07888998 0.01157424 6.816
## my$bctIDCMhaploid0.001d1A4B 0.00800659 0.01199416 0.668
## my$bctIDCMhaploid0.001d1A5 0.07567154 0.01371413 5.518
## my$bctIDCMhaploid0.001d1A5B 0.05376713 0.01259551 4.269
## my$bctIDCMhaploid0.001d1A6 0.02913551 0.01052784 2.767
## my$bctIDCMhaploid0.001d1A6B 0.02748147 0.00908708 3.024
## my$bctIDCMhaploid0.001d1A7 0.28424691 0.22971593 1.237
## my$bctIDCMhaploid0.001d1A7B 0.37549211 0.27832375 1.349
## my$bctIDCMhaploid0.001d1H2 0.05734718 0.01492184 3.843
## my$bctIDCMhaploid0.001d1H3 0.00588798 0.01683299 0.350
## my$bctIDCMhaploid0.001d1H3B 0.02271945 0.01846922 1.230
## my$bctIDCMhaploid0.001d1H4 0.06491278 0.03830520 1.695
## my$bctIDCMhaploid0.001d1H4B 0.01454690 0.01990335 0.731
## my$bctIDCMhaploid0.001d1H5 0.24925362 0.21028584 1.185
## my$bctIDCMhaploid0.001d1H5B 0.07768822 0.01659155 4.682
## my$bctIDCMhaploid0.001d1H6 0.10010351 0.02954796 3.388
## my$bctIDCMhaploid0.001d1H6B 0.11256605 0.02312772 4.867
## my$bctIDCMhaploid0.001d1H7 0.08865934 0.02084663 4.253
## my$bctIDCMhaploid0.001d1H7B 0.07939256 0.01932983 4.107
## Pr(>|t|)
## my$bctIDCM+Ethanoldiploid0.001d1A2 0.854419
## my$bctIDCM+Ethanoldiploid0.001d1A4 0.196832
## my$bctIDCM+Ethanoldiploid0.001d1A5 0.908990
## my$bctIDCM+Ethanoldiploid0.001d1A6 0.465352
## my$bctIDCM+Ethanoldiploid0.001d1A7 0.595903
## my$bctIDCM+Ethanoldiploid0.001d1D10 0.001570 **
## my$bctIDCM+Ethanoldiploid0.001d1D11 0.006650 **
## my$bctIDCM+Ethanoldiploid0.001d1D12 0.018906 *
## my$bctIDCM+Ethanoldiploid0.001d1D9 0.248287
## my$bctIDCM+Ethanoldiploid0.001d1E10 0.00002165288566661 ***
## my$bctIDCM+Ethanoldiploid0.001d1E11 0.005182 **
## my$bctIDCM+Ethanoldiploid0.001d1E12 0.012147 *
## my$bctIDCM+Ethanoldiploid0.001d1E9 0.350903
## my$bctIDCM+Ethanoldiploid0.001d1F1 0.286224
## my$bctIDCM+Ethanoldiploid0.001d1F2 0.084602 .
## my$bctIDCM+Ethanoldiploid0.001d1G1 0.269859
## my$bctIDCM+Ethanoldiploid0.001d1G2 0.535545
## my$bctIDCM+Ethanoldiploid0.001d1H2 0.450992
## my$bctIDCM+Ethanoldiploid0.001d1H4 0.900820
## my$bctIDCM+Ethanoldiploid0.001d1H5 0.801543
## my$bctIDCM+Ethanoldiploid0.001d1H6 0.794250
## my$bctIDCM+Ethanoldiploid0.001d1H7 0.829265
## my$bctIDCM+Saltdiploid0.001d1A2 0.000291 ***
## my$bctIDCM+Saltdiploid0.001d1A3 0.153770
## my$bctIDCM+Saltdiploid0.001d1A4 0.023413 *
## my$bctIDCM+Saltdiploid0.001d1A5 0.043309 *
## my$bctIDCM+Saltdiploid0.001d1A7 0.051883 .
## my$bctIDCM+Saltdiploid0.001d1F3 0.00000000001514729 ***
## my$bctIDCM+Saltdiploid0.001d1F4 0.00000000000000266 ***
## my$bctIDCM+Saltdiploid0.001d1F5 0.208173
## my$bctIDCM+Saltdiploid0.001d1F6 < 0.0000000000000002 ***
## my$bctIDCM+Saltdiploid0.001d1F7 0.00000000000263293 ***
## my$bctIDCM+Saltdiploid0.001d1F8 0.00000000000113446 ***
## my$bctIDCM+Saltdiploid0.001d1G3 0.010247 *
## my$bctIDCM+Saltdiploid0.001d1G4 0.003166 **
## my$bctIDCM+Saltdiploid0.001d1G5 0.00000104573824082 ***
## my$bctIDCM+Saltdiploid0.001d1G6 0.006566 **
## my$bctIDCM+Saltdiploid0.001d1G7 0.00003974952106726 ***
## my$bctIDCM+Saltdiploid0.001d1G8 0.000151 ***
## my$bctIDCM+Saltdiploid0.001d1H2 0.747562
## my$bctIDCM+Saltdiploid0.001d1H3 0.191568
## my$bctIDCM+Saltdiploid0.001d1H4 0.269265
## my$bctIDCM+Saltdiploid0.001d1H5 0.767557
## my$bctIDCM+Saltdiploid0.001d1H7 0.000368 ***
## my$bctIDCMdiploid0.00025d1A2 0.329882
## my$bctIDCMdiploid0.00025d1A3 0.557181
## my$bctIDCMdiploid0.00025d1A4 0.903664
## my$bctIDCMdiploid0.00025d1A5 0.077226 .
## my$bctIDCMdiploid0.00025d1A6 0.358086
## my$bctIDCMdiploid0.00025d1A7 0.786112
## my$bctIDCMdiploid0.00025d1B10 0.624463
## my$bctIDCMdiploid0.00025d1B11 0.166007
## my$bctIDCMdiploid0.00025d1B12 0.103764
## my$bctIDCMdiploid0.00025d1B9 0.107440
## my$bctIDCMdiploid0.00025d1C10 0.368373
## my$bctIDCMdiploid0.00025d1C11 0.048000 *
## my$bctIDCMdiploid0.00025d1C12 0.259774
## my$bctIDCMdiploid0.00025d1C9 0.662997
## my$bctIDCMdiploid0.00025d1D2 0.946757
## my$bctIDCMdiploid0.00025d1E2 0.221035
## my$bctIDCMdiploid0.00025d1H2 0.666246
## my$bctIDCMdiploid0.00025d1H3 0.792714
## my$bctIDCMdiploid0.00025d1H4 0.447657
## my$bctIDCMdiploid0.00025d1H5 0.065659 .
## my$bctIDCMdiploid0.00025d1H6 0.759244
## my$bctIDCMdiploid0.00025d1H7 0.945372
## my$bctIDCMdiploid0.001d1A11 0.047218 *
## my$bctIDCMdiploid0.001d1A2 0.568387
## my$bctIDCMdiploid0.001d1A2B 0.445453
## my$bctIDCMdiploid0.001d1A3 0.982727
## my$bctIDCMdiploid0.001d1A3B 0.774047
## my$bctIDCMdiploid0.001d1A4 0.174949
## my$bctIDCMdiploid0.001d1A4B 0.908856
## my$bctIDCMdiploid0.001d1A5 0.061817 .
## my$bctIDCMdiploid0.001d1A5B 0.726909
## my$bctIDCMdiploid0.001d1A6 0.074694 .
## my$bctIDCMdiploid0.001d1A6B 0.476265
## my$bctIDCMdiploid0.001d1A7 0.001518 **
## my$bctIDCMdiploid0.001d1A8 0.196716
## my$bctIDCMdiploid0.001d1A9 0.044951 *
## my$bctIDCMdiploid0.001d1B1 0.006576 **
## my$bctIDCMdiploid0.001d1B2 0.003039 **
## my$bctIDCMdiploid0.001d1C1 0.002576 **
## my$bctIDCMdiploid0.001d1C2 0.013632 *
## my$bctIDCMdiploid0.001d1D4 0.096546 .
## my$bctIDCMdiploid0.001d1D5 0.152415
## my$bctIDCMdiploid0.001d1D6 0.315226
## my$bctIDCMdiploid0.001d1D7 0.310496
## my$bctIDCMdiploid0.001d1D8 0.240516
## my$bctIDCMdiploid0.001d1E4 0.700111
## my$bctIDCMdiploid0.001d1E5 0.197395
## my$bctIDCMdiploid0.001d1E6 0.023170 *
## my$bctIDCMdiploid0.001d1E7 0.008807 **
## my$bctIDCMdiploid0.001d1E8 0.690775
## my$bctIDCMdiploid0.001d1H11 0.038086 *
## my$bctIDCMdiploid0.001d1H2 0.502150
## my$bctIDCMdiploid0.001d1H2B 0.304231
## my$bctIDCMdiploid0.001d1H3 0.767194
## my$bctIDCMdiploid0.001d1H3B 0.998072
## my$bctIDCMdiploid0.001d1H4 0.721741
## my$bctIDCMdiploid0.001d1H4B 0.915822
## my$bctIDCMdiploid0.001d1H5 0.374428
## my$bctIDCMdiploid0.001d1H5B 0.839864
## my$bctIDCMdiploid0.001d1H6 0.011740 *
## my$bctIDCMdiploid0.001d1H6B 0.055013 .
## my$bctIDCMdiploid0.001d1H7 0.722004
## my$bctIDCMdiploid0.001d1H8 0.534803
## my$bctIDCMdiploid0.001d1H9 0.407275
## my$bctIDCMdiploid0.004d1A2 0.965323
## my$bctIDCMdiploid0.004d1A3 0.522127
## my$bctIDCMdiploid0.004d1A4 0.003677 **
## my$bctIDCMdiploid0.004d1A5 0.004378 **
## my$bctIDCMdiploid0.004d1A6 0.262755
## my$bctIDCMdiploid0.004d1A7 0.153420
## my$bctIDCMdiploid0.004d1B3 0.089106 .
## my$bctIDCMdiploid0.004d1B4 0.060315 .
## my$bctIDCMdiploid0.004d1B5 0.001775 **
## my$bctIDCMdiploid0.004d1B6 0.002307 **
## my$bctIDCMdiploid0.004d1B7 0.00000056011850773 ***
## my$bctIDCMdiploid0.004d1C3 0.003811 **
## my$bctIDCMdiploid0.004d1C4 0.096605 .
## my$bctIDCMdiploid0.004d1C5 0.000868 ***
## my$bctIDCMdiploid0.004d1C6 0.00000684943418923 ***
## my$bctIDCMdiploid0.004d1C7 0.186138
## my$bctIDCMdiploid0.004d1H2 0.335547
## my$bctIDCMdiploid0.004d1H3 0.333005
## my$bctIDCMdiploid0.004d1H4 0.857174
## my$bctIDCMdiploid0.004d1H5 0.001720 **
## my$bctIDCMdiploid0.004d1H6 0.023234 *
## my$bctIDCMdiploid0.004d1H7 0.712242
## my$bctIDCMhaploid0.001d1A2 0.337921
## my$bctIDCMhaploid0.001d1A3 0.034829 *
## my$bctIDCMhaploid0.001d1A3B 0.020091 *
## my$bctIDCMhaploid0.001d1A4 0.00000000002976149 ***
## my$bctIDCMhaploid0.001d1A4B 0.504764
## my$bctIDCMhaploid0.001d1A5 0.00000005766073013 ***
## my$bctIDCMhaploid0.001d1A5B 0.00002392620167394 ***
## my$bctIDCMhaploid0.001d1A6 0.005879 **
## my$bctIDCMhaploid0.001d1A6B 0.002633 **
## my$bctIDCMhaploid0.001d1A7 0.216581
## my$bctIDCMhaploid0.001d1A7B 0.177968
## my$bctIDCMhaploid0.001d1H2 0.000139 ***
## my$bctIDCMhaploid0.001d1H3 0.726659
## my$bctIDCMhaploid0.001d1H3B 0.219285
## my$bctIDCMhaploid0.001d1H4 0.090830 .
## my$bctIDCMhaploid0.001d1H4B 0.465230
## my$bctIDCMhaploid0.001d1H5 0.236513
## my$bctIDCMhaploid0.001d1H5B 0.00000374585588773 ***
## my$bctIDCMhaploid0.001d1H6 0.000766 ***
## my$bctIDCMhaploid0.001d1H6B 0.00000156280464386 ***
## my$bctIDCMhaploid0.001d1H7 0.00002561158235811 ***
## my$bctIDCMhaploid0.001d1H7B 0.00004746151175424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.922 on 456 degrees of freedom
## Multiple R-squared: 0.7186, Adjusted R-squared: 0.6248
## F-statistic: 7.66 on 152 and 456 DF, p-value: < 0.00000000000000022
myrmse <- sqrt(mean(residuals(mymod)^2)) * 100 # this is the rmse from the fitness equivalence model*100 --> effect size of 1 corresonds to an ~4.307 fitness dif
myeffectsize <- 1/myrmse # effect size of 1 is a 4.307 fitnesss change, so for a fitness change of 1.00, expect effect size of 1/myrmse
# power to detect treatment effects ------------------------------
myv <- (22 * 2) - 1 - 1
mypowersize <- (pwr.f2.test(u = 1, v = myv, f2 = myeffectsize, sig.level = 0.05))$power # powersize for plotting below, where 22 is the number of BCs in any given treatment.
# prepare plot data ----------------------------------------
mypower <- matrix(nrow = 100, ncol = 5)
colnames(mypower) <- c("u", "v", "power", "f2", "sig.level")
mypower <- as.data.frame(mypower)
mypower$u <- 1
mypower$v <- c(myv, myv * 0.9, myv * 0.8, myv * 0.7, myv * 0.6, myv * 0.5, myv *
0.4, myv * 0.3, myv * 0.2, myv * 0.1)
mypower$power <- NA
mypower$f2 <- c(rep(myeffectsize * 2, 10), rep(myeffectsize * 1.75, 10), rep(myeffectsize *
1.5, 10), rep(myeffectsize * 1.25, 10), rep(myeffectsize * 1, 10), rep(myeffectsize *
0.75, 10), rep(myeffectsize * 0.5, 10), rep(myeffectsize * 0.25, 10), rep(myeffectsize *
0.1, 10), rep(myeffectsize * 0.05, 10))
mypower$sig.level <- 0.05
mypower$group <- c(rep(1, 10), rep(2, 10), rep(3, 10), rep(4, 10), rep(5, 10),
rep(6, 10), rep(7, 10), rep(8, 10), rep(9, 10), rep(10, 10))
mypower$group <- as.factor(mypower$group)
for (i in 1:nrow(mypower)) {
pow <- pwr.f2.test(u = mypower$u[i], v = mypower$v[i], f2 = mypower$f2[i],
sig.level = mypower$sig.level[i])
mypower$power[i] <- pow$power
}
mypower$n <- mypower$v + 2
mypower$n <- factor(mypower$n, levels = c(myv + 2, (myv * 0.9) + 2, (myv * 0.8) +
2, (myv * 0.7) + 2, (myv * 0.6) + 2, (myv * 0.5) + 2, (myv * 0.4) + 2, (myv *
0.3) + 2, (myv * 0.2) + 2, (myv * 0.1) + 2))
mypower$v <- factor(mypower$v, levels = c(myv, myv * 0.9, myv * 0.8, myv * 0.7,
myv * 0.6, myv * 0.5, myv * 0.4, myv * 0.3, myv * 0.2, myv * 0.1))
mypower <- mypower[mypower$n == 44, ]
mypower$f2 <- mypower$f2 * myrmse
mypower_b_250 <- mypower
mypower_b_250$exp <- "250 Generation Evolution Fitness Assays"
myeffectsize_b_250 <- myeffectsize * myrmse
mypowersize_b_250 <- mypowersize
mypower <- rbind(mypower_b_poc, mypower_b_250)
mypower$exp <- as.factor(mypower$exp)
mypower$exp <- relevel(mypower$exp, ref = "POC Fitness Assays")
# Plotting ------------------------------------------------- plot effect
# size x power -------------
p <- ggplot(mypower, aes(x = f2, y = power, group = exp)) + geom_vline(xintercept = myeffectsize_b_poc,
linetype = "dashed", color = "darkgray") + geom_vline(xintercept = myeffectsize_b_250,
linetype = "dashed", color = "darkgray") + geom_line(aes(color = exp)) +
geom_point(aes(color = exp)) + geom_point(x = myeffectsize_b_poc, y = mypowersize_b_poc,
pch = 1, size = 5, color = "darkgray") + geom_point(x = myeffectsize_b_250,
y = mypowersize_b_250, pch = 1, size = 5, color = "darkgray") + theme_classic() +
labs(tag = "B") + labs(color = "n (total barcodes \n in two treatments))") +
scale_x_continuous(name = "Fitness difference between treatments \n ", breaks = c(0.05,
0.1, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2), labels = c("0.05%", "",
"0.25%", "0.5%", "0.75%", "1.0%", "1.25%", "1.5%", "1.75%", "2.0%")) +
scale_color_manual(values = setpalette) + theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pB <- p
pB
# Plot using grid and output
# ---------------------------------------------------
mywidth <- 3.345
myheight <- 6.69
pdf(file = "000_Figure_3.pdf", width = mywidth, height = myheight)
grid.arrange(pA, pB, legend1, layout_matrix = rbind(c(1, 1, 1, 1), c(1, 1, 1,
1), c(1, 1, 1, 1), c(1, 1, 1, 1), c(2, 2, 2, 2), c(2, 2, 2, 2), c(2, 2,
2, 2), c(2, 2, 2, 2), c(3, 3, 3, 3)))
## Warning: Removed 12 rows containing missing values (geom_path).
## Warning: Removed 12 rows containing missing values (geom_point).
dev.off() # one column wide, equal height
## quartz_off_screen
## 2
# ------------------------------------------------------------------------------
rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7, my8, my9, my10,
mymod, mypower, mypower2, p, pA, pB, pC, pD, pow, i, myeffectsize, myheight,
mypowersize, myrmse, myv, mywidth, psd, mypower_a_250, mypower_a_poc, mypower_b_250,
mypower_b_poc, myeffectsize_b_250, myeffectsize_b_poc, mypowersize_b_250,
mypowersize_b_poc, psd250, psdpoc)
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'legend2' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'mypower2' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'pC' not found
## Warning in rm(legend1, legend2, my, my1, my2, my3, my4, my5, my6, my7,
## my8, : object 'pD' not found
Analysis: Assess effects of evoluitonary treatment on change in fitness in 250-generations of experimental evolution.
my <- myfa[order(myfa$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ----------------------------------------------------
mymod <- lmer(I(scale(deltafit)) ~ I(scale(ccdeltafit)) + treatment + (1 | bctID),
weights = rdeltafit, data = my, control = lmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun = 20000)))
# model output
# -----------------------------------------------------------------=
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
show.r2 = F, file = "000_Additional_File_13.html")
| Â | I(scale(deltafit)) | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | -0.31415 | -0.43012 – -0.19819 | -5.30957 | 0.00000047 |
| I(scale(ccdeltafit)) | -0.27053 | -0.30697 – -0.23408 | -14.54794 | <0.001 |
| CM+Ethanoldiploid 0.001 | -0.29452 | -0.49136 – -0.09767 | -2.93240 | 0.00398690 |
| CM+Saltdiploid 0.001 | 0.96163 | 0.75021 – 1.17306 | 8.91470 | <0.001 |
| C Mdiploid 0.00025 | -0.08752 | -0.28584 – 0.11081 | -0.86489 | 0.38870062 |
| C Mdiploid 0.004 | 0.34621 | 0.14533 – 0.54710 | 3.37792 | 0.00094905 |
| C Mhaploid 0.001 | 0.27966 | 0.06625 – 0.49307 | 2.56846 | 0.01130558 |
| Random Effects | ||||
| σ2 | 367.71 | |||
| τ00 bctID | 0.12 | |||
| ICC | 0.00 | |||
| N bctID | 152 | |||
| Observations | 608 | |||
# -----------------------------------------------------------------------------
my <- myfa_w[order(myfa_w$bctID), ] # placeholder dataframe
my <- my[my$sedeltafit < 0.029, ]
min(my$mndeltafit)
## [1] -0.03049231
max(my$mndeltafit)
## [1] 0.2345108
myx <- my$treatment
myy <- my$mndeltafit
myr <- my$mnrdeltafit
myc <- my$treatment
mytitle <- NULL
myylab <- "Fitness change"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.345
mywidth <- 6.69
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 1:1000", "CM+EtOH \n Diploid \n 1:1000",
"CM+NaCl \n Diploid \n 1:1000", "CM \n Diploid \n 1:4000", "CM \n Diploid \n 1:250",
"CM \n Haploid \n 1:1000")) + scale_y_continuous(breaks = seq(-0.05,
0.25, by = 0.05), labels = c("-0.05", "0", "0.05", "0.1", "0.15", "0.2",
"0.25"), limits = c(-0.05, 0.25)) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(2, 3, 4, 5, 6), y = c(0.065, 0.25, 0.045, 0.095, 0.115), label = c("**",
"***", "", "***", "*"), size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Figure_5.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, weights)
rm(p, myc, myfill, myfillapha, myheight, mylinealpha, myoutline, mypalette,
myptalpha, myptsize, myr, mytextsize, mytitle, myx, myxlab, mywidth, myy,
myylab)
Analysis: Calculate fixation rate summaries by treatment for the 250-generation experiment.
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# Review Results
# --------------------------------------------------------------
paste0("proportion of wells that attained fixation (n=76 total wells)= ", nrow(my[my$fixed ==
T, ])/nrow(my)) # prop fixed
## [1] "proportion of wells that attained fixation (n=76 total wells)= 0.118421052631579"
# CM diploid 1:1000
myt <- my[my$treatment == "CMdiploid0.001", ]
paste0("n wells in CMdiploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CMdiploid0.001 = 21"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 1"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0.0476190476190476"
# CM haploid 1:1000
myt <- my[my$treatment == "CMhaploid0.001", ]
paste0("n wells in CMhaploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CMhaploid0.001 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 5"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0.454545454545455"
# CM+Salt diploid 1:1000
myt <- my[my$treatment == "CM+Saltdiploid0.001", ]
paste0("n wells in CM+Saltdiploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CM+Saltdiploid0.001 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 3"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0.272727272727273"
# CM+Ethanol 1:1000
myt <- my[my$treatment == "CM+Ethanoldiploid0.001", ]
paste0("n wells in CM+Ethanoldiploid0.001 = ", nrow(myt)) # wells
## [1] "n wells in CM+Ethanoldiploid0.001 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 0"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0"
# CM diploid 1:250
myt <- my[my$treatment == "CMdiploid0.004", ]
paste0("n wells in CMdiploid0.004 = ", nrow(myt)) # wells
## [1] "n wells in CMdiploid0.004 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 0"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0"
# CM diploid 1:4000
myt <- my[my$treatment == "CMdiploid0.00025", ]
paste0("n wells in CMdiploid0.00025 = ", nrow(myt)) # wells
## [1] "n wells in CMdiploid0.00025 = 11"
paste0("number of fixed wells in treatment = ", nrow(myt[myt$fixed, ])) # number fixed
## [1] "number of fixed wells in treatment = 0"
paste0("proportion of fixed wells in treatment = ", nrow(myt[myt$fixed, ])/nrow(myt)) # prop fixed
## [1] "proportion of fixed wells in treatment = 0"
# -----------------------------------------------------------------------------
rm(my, myt)
Analysis: Assess effects of evolutionary treatment on T-MAX (generation of maximum deviation from generation-0 barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tmaxdev ~ my$treatment
# + my$ccmaxdev_t0 + my$diff0, weights = my$rmaxdev_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$tmaxdev ~ my$treatment +
# my$diff0, weights = my$rmaxdev_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$tmaxdev ~ my$treatment + my$ccmaxdev, weights =
# my$rmaxdev); summary(mymod) # drop c as least significant term
mymod <- lm(my$tmaxdev ~ my$treatment, weights = my$rmaxdev)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$tmaxdev ~ my$treatment, weights = my$rmaxdev)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -45513 -3616 1146 3910 16785
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 233.5992 14.7420 15.846
## my$treatmentCM+Ethanoldiploid0.001 -20.8306 17.1490 -1.215
## my$treatmentCM+Saltdiploid0.001 -48.7017 18.3948 -2.648
## my$treatmentCMdiploid0.00025 2.2063 20.8325 0.106
## my$treatmentCMdiploid0.004 -43.3530 27.8363 -1.557
## my$treatmentCMhaploid0.001 0.8156 16.9681 0.048
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## my$treatmentCM+Ethanoldiploid0.001 0.229
## my$treatmentCM+Saltdiploid0.001 0.010 *
## my$treatmentCMdiploid0.00025 0.916
## my$treatmentCMdiploid0.004 0.124
## my$treatmentCMhaploid0.001 0.962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9517 on 70 degrees of freedom
## Multiple R-squared: 0.1972, Adjusted R-squared: 0.1398
## F-statistic: 3.438 on 5 and 70 DF, p-value: 0.007779
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxdev), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.19716
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.1398143
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_14.html")
| Â | my$tmaxdev | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 233.59916 | 204.70539 – 262.49293 | 15.84584 | <0.001 |
| CM+Ethanoldiploid 0.001 | -20.83060 | -54.44209 – 12.78089 | -1.21468 | 0.22856965 |
| CM+Saltdiploid 0.001 | -48.70167 | -84.75481 – -12.64854 | -2.64758 | 0.01000874 |
| C Mdiploid 0.00025 | 2.20631 | -38.62458 – 43.03720 | 0.10591 | 0.91595897 |
| C Mdiploid 0.004 | -43.35303 | -97.91111 – 11.20506 | -1.55743 | 0.12387850 |
| C Mhaploid 0.001 | 0.81564 | -32.44114 – 34.07241 | 0.04807 | 0.96179819 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.197 / 0.140 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tmaxdev
myr <- my$rmaxdev
myc <- my$treatment
mytitle <- NULL
myylab <- "T-MAX (Generation)"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
# geom_hline(yintercept = 0, lty = 'dotted') +
coord_flip() + theme_classic() + theme(legend.position = "none") + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + scale_x_discrete(labels = c("CM \n Diploid \n 0.001",
"CM+EtOH \n Diploid \n 0.001", "CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025",
"CM \n Diploid \n 0.004", "CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(3), y = c(255), label = "*", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_15.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)
Analysis: Assess effects of evolutionary treatment on M-MAX (magnitude of maximum deviation from generation-0 barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$mmaxdev ~ my$treatment
# + my$ccmaxdev_t0 + my$diff0, weights = my$rmaxdev_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$mmaxdev ~ my$treatment +
# my$diff0, weights = my$rmaxdev_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$mmaxdev ~ my$treatment + my$ccmaxdev, weights =
# my$rmaxdev); summary(mymod) # drop c as least significant term
mymod <- lm(my$mmaxdev ~ my$treatment, weights = my$rmaxdev)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$mmaxdev ~ my$treatment, weights = my$rmaxdev)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -80.362 -11.841 -1.923 18.199 121.427
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22746 0.05437 4.184 0.0000820
## my$treatmentCM+Ethanoldiploid0.001 -0.06495 0.06325 -1.027 0.30799
## my$treatmentCM+Saltdiploid0.001 0.29240 0.06784 4.310 0.0000524
## my$treatmentCMdiploid0.00025 0.11117 0.07683 1.447 0.15240
## my$treatmentCMdiploid0.004 0.03904 0.10266 0.380 0.70491
## my$treatmentCMhaploid0.001 0.17791 0.06258 2.843 0.00585
##
## (Intercept) ***
## my$treatmentCM+Ethanoldiploid0.001
## my$treatmentCM+Saltdiploid0.001 ***
## my$treatmentCMdiploid0.00025
## my$treatmentCMdiploid0.004
## my$treatmentCMhaploid0.001 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.1 on 70 degrees of freedom
## Multiple R-squared: 0.4564, Adjusted R-squared: 0.4176
## F-statistic: 11.76 on 5 and 70 DF, p-value: 0.00000002882
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxdev), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.4564297
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.4176032
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_16.html")
| Â | my$mmaxdev | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 0.22746 | 0.12089 – 0.33402 | 4.18350 | 0.00008197 |
| CM+Ethanoldiploid 0.001 | -0.06495 | -0.18891 – 0.05901 | -1.02693 | 0.30798766 |
| CM+Saltdiploid 0.001 | 0.29240 | 0.15943 – 0.42537 | 4.30998 | 0.00005236 |
| C Mdiploid 0.00025 | 0.11117 | -0.03942 – 0.26175 | 1.44685 | 0.15240115 |
| C Mdiploid 0.004 | 0.03904 | -0.16218 – 0.24025 | 0.38025 | 0.70491018 |
| C Mhaploid 0.001 | 0.17791 | 0.05526 – 0.30057 | 2.84299 | 0.00585276 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.456 / 0.418 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$mmaxdev
myr <- my$rmaxdev
myc <- my$treatment
mytitle <- NULL
myylab <- "M-MAX"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myfillapha <- 0.35
myptalpha <- 0.35
mylinealpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(3, 6), y = c(0.91, 0.765), label = c("***", "**"), size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_17.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab, myfillapha,
mylinealpha)
Analysis: Assess effects of evolutionary treatment on T-MAX-RATE (generation of maximum rate of change in barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tmaxrate ~ my$treatment
# + my$ccmaxrate_t0 + my$diff0, weights = my$rmaxrate_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$tmaxrate ~ my$treatment +
# my$diff0, weights = my$rmaxrate_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$tmaxrate ~ my$treatment + my$ccmaxrate, weights =
# my$rmaxrate); summary(mymod) # drop c as least significant term
mymod <- lm(my$tmaxrate ~ my$treatment, weights = my$rmaxrate)
summary(mymod) # Final model!
##
## Call:
## lm(formula = my$tmaxrate ~ my$treatment, weights = my$rmaxrate)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -36500 -2877 985 3554 25478
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 235.251 7.709 30.518
## my$treatmentCM+Ethanoldiploid0.001 11.439 10.407 1.099
## my$treatmentCM+Saltdiploid0.001 -19.045 12.176 -1.564
## my$treatmentCMdiploid0.00025 9.910 17.741 0.559
## my$treatmentCMdiploid0.004 6.309 11.672 0.541
## my$treatmentCMhaploid0.001 -52.717 11.261 -4.681
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## my$treatmentCM+Ethanoldiploid0.001 0.275
## my$treatmentCM+Saltdiploid0.001 0.122
## my$treatmentCMdiploid0.00025 0.578
## my$treatmentCMdiploid0.004 0.591
## my$treatmentCMhaploid0.001 0.0000135 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9066 on 70 degrees of freedom
## Multiple R-squared: 0.3847, Adjusted R-squared: 0.3408
## F-statistic: 8.755 on 5 and 70 DF, p-value: 0.000001732
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxrate), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.3847431
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.3407962
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_18.html")
| Â | my$tmaxrate | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 235.25147 | 220.14297 – 250.35998 | 30.51820 | <0.001 |
| CM+Ethanoldiploid 0.001 | 11.43864 | -8.95850 – 31.83578 | 1.09914 | 0.27547276 |
| CM+Saltdiploid 0.001 | -19.04522 | -42.90918 – 4.81874 | -1.56420 | 0.12228104 |
| C Mdiploid 0.00025 | 9.90963 | -24.86202 – 44.68129 | 0.55857 | 0.57823532 |
| C Mdiploid 0.004 | 6.30932 | -16.56811 – 29.18674 | 0.54053 | 0.59054432 |
| C Mhaploid 0.001 | -52.71656 | -74.78791 – -30.64520 | -4.68130 | 0.00001354 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.385 / 0.341 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tmaxrate
myr <- my$rmaxrate
myc <- my$treatment
mytitle <- NULL
myylab <- "T-MAX-RATE"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
# geom_hline(yintercept = 0, lty = 'dotted') +
coord_flip() + theme_classic() + theme(legend.position = "none") + ggtitle(mytitle) +
ylab(myylab) + xlab(myxlab) + scale_x_discrete(labels = c("CM \n Diploid \n 0.001",
"CM+EtOH \n Diploid \n 0.001", "CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025",
"CM \n Diploid \n 0.004", "CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(6), y = c(257), label = "***", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_19.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)
Analysis: Assess effects of evolutionary treatment on M-MAX-RATE (magnitude of maximum rate of change in barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$mmaxrate ~ my$treatment
# + my$ccmaxrate_t0 + my$diff0, weights = my$rmaxrate_rt0); summary(mymod) #
# drop c as least significant term mymod <- glm(my$mmaxrate ~ my$treatment +
# my$diff0, weights = my$rmaxrate_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$mmaxrate ~ my$treatment + my$ccmaxrate, weights =
# my$rmaxrate); summary(mymod) # drop c as least significant term
mymod <- lm(my$mmaxrate ~ my$treatment, weights = my$rmaxrate)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$mmaxrate ~ my$treatment, weights = my$rmaxrate)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -4.0806 -0.8103 -0.1020 0.8827 4.2056
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.009582 0.001258 7.620
## my$treatmentCM+Ethanoldiploid0.001 -0.002035 0.001698 -1.199
## my$treatmentCM+Saltdiploid0.001 0.003154 0.001986 1.588
## my$treatmentCMdiploid0.00025 0.006305 0.002894 2.178
## my$treatmentCMdiploid0.004 -0.002101 0.001904 -1.104
## my$treatmentCMhaploid0.001 -0.002801 0.001837 -1.525
## Pr(>|t|)
## (Intercept) 0.0000000000916 ***
## my$treatmentCM+Ethanoldiploid0.001 0.2347
## my$treatmentCM+Saltdiploid0.001 0.1168
## my$treatmentCMdiploid0.00025 0.0328 *
## my$treatmentCMdiploid0.004 0.2736
## my$treatmentCMhaploid0.001 0.1319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.479 on 70 degrees of freedom
## Multiple R-squared: 0.2098, Adjusted R-squared: 0.1534
## F-statistic: 3.718 on 5 and 70 DF, p-value: 0.004837
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rmaxrate), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.2098238
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.1533827
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_20.html")
| Â | my$mmaxrate | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 0.00958 | 0.00712 – 0.01205 | 7.61961 | <0.001 |
| CM+Ethanoldiploid 0.001 | -0.00204 | -0.00536 – 0.00129 | -1.19871 | 0.23468475 |
| CM+Saltdiploid 0.001 | 0.00315 | -0.00074 – 0.00705 | 1.58776 | 0.11684830 |
| C Mdiploid 0.00025 | 0.00630 | 0.00063 – 0.01198 | 2.17830 | 0.03275392 |
| C Mdiploid 0.004 | -0.00210 | -0.00583 – 0.00163 | -1.10353 | 0.27357441 |
| C Mhaploid 0.001 | -0.00280 | -0.00640 – 0.00080 | -1.52455 | 0.13187664 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.210 / 0.153 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$mmaxrate
myr <- my$rmaxrate
myc <- my$treatment
mytitle <- NULL
myylab <- "M-MAX-RATE"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(4), y = c(0.0325), label = "*", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_21.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)
Analysis: Assess effects of evolutionary treatment on T-MAX-DIFF (generation of maximum difference in sympatric barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tmaxdiff ~ my$treatment
# + my$ccmaxdiff_t0 + my$diff0, weights = my$rmaxdiff_rt0); summary(mymod) #
# drop c as least significant term
mymod <- lm(my$tmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
summary(mymod) # # FINAL MODEL!
##
## Call:
## lm(formula = my$tmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -58426 -5302 574 8332 59655
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 241.970 34.101 7.096
## my$treatmentCM+Ethanoldiploid0.001 -22.732 35.785 -0.635
## my$treatmentCM+Saltdiploid0.001 -5.533 38.822 -0.143
## my$treatmentCMdiploid0.00025 11.312 77.413 0.146
## my$treatmentCMdiploid0.004 -23.716 63.676 -0.372
## my$treatmentCMhaploid0.001 1.612 35.479 0.045
## my$diff0 -328.617 81.224 -4.046
## Pr(>|t|)
## (Intercept) 0.000000000892 ***
## my$treatmentCM+Ethanoldiploid0.001 0.527381
## my$treatmentCM+Saltdiploid0.001 0.887074
## my$treatmentCMdiploid0.00025 0.884245
## my$treatmentCMdiploid0.004 0.710705
## my$treatmentCMhaploid0.001 0.963902
## my$diff0 0.000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19160 on 69 degrees of freedom
## Multiple R-squared: 0.2342, Adjusted R-squared: 0.1676
## F-statistic: 3.517 on 6 and 69 DF, p-value: 0.004283
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rtcc), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.2342223
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.1676329
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_22.html")
|  | my\(tmaxdiff</th> </tr> <tr> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; text-align:left; ">Predictors</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Estimates</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">CI</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Statistic</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">p</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">241.97046</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">175.13346 – 308.80745</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">7.09567</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong><0.001</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Ethanoldiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-22.73154</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-92.86866 – 47.40558</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.63523</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.52738134</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Saltdiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-5.53349</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-81.62393 – 70.55695</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.14253</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.88707398</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.00025</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">11.31234</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-140.41438 – 163.03905</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.14613</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.88424511</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.004</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-23.71583</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-148.51929 – 101.08763</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.37244</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.71070458</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mhaploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">1.61153</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-67.92574 – 71.14880</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.04542</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.96390191</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">my\)diff 0 | -328.61667 | -487.81355 – -169.41979 | -4.04579 | 0.00013406 | |||
|---|---|---|---|---|---|---|---|---|
| Observations | 76 | |||||||
| R2 / R2 adjusted | 0.234 / 0.168 | |||||||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tmaxdiff
myr <- my$rmaxdiff_rt0
myc <- my$treatment
mytitle <- NULL
myylab <- "T-MAX-DIFF"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0))
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_23.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)
Analysis: Assess effects of evolutionary treatment on M-MAX-DIFF (magnitude of maximum difference in sympatric barcoded strain abundance)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$mmaxdiff ~ my$treatment
# + my$ccmaxdiff_t0 + my$diff0, weights = my$rmaxdiff_rt0); summary(mymod) #
# drop c as least significant term
mymod <- lm(my$mmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
summary(mymod) # # FINAL MODEL!
##
## Call:
## lm(formula = my$mmaxdiff ~ my$treatment + my$diff0, weights = my$rmaxdiff_rt0)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -90.008 -8.179 2.215 10.833 47.792
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18985 0.04004 4.741 0.00001105
## my$treatmentCM+Ethanoldiploid0.001 0.00454 0.04202 0.108 0.914271
## my$treatmentCM+Saltdiploid0.001 0.17472 0.04559 3.833 0.000277
## my$treatmentCMdiploid0.00025 0.15524 0.09090 1.708 0.092165
## my$treatmentCMdiploid0.004 0.03311 0.07477 0.443 0.659311
## my$treatmentCMhaploid0.001 0.20429 0.04166 4.904 0.00000601
## my$diff0 0.29644 0.09538 3.108 0.002734
##
## (Intercept) ***
## my$treatmentCM+Ethanoldiploid0.001
## my$treatmentCM+Saltdiploid0.001 ***
## my$treatmentCMdiploid0.00025 .
## my$treatmentCMdiploid0.004
## my$treatmentCMhaploid0.001 ***
## my$diff0 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.49 on 69 degrees of freedom
## Multiple R-squared: 0.4789, Adjusted R-squared: 0.4336
## F-statistic: 10.57 on 6 and 69 DF, p-value: 0.00000002701
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rtcc), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.4789295
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.433619
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_24.html")
|  | my\(mmaxdiff</th> </tr> <tr> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; text-align:left; ">Predictors</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Estimates</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">CI</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">Statistic</td> <td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal; ">p</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.18985</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.11137 – 0.26833</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">4.74128</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00001105</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Ethanoldiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.00454</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.07782 – 0.08690</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.10805</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.91427138</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">CM+Saltdiploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.17472</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.08537 – 0.26406</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">3.83269</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00027655</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.00025</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.15524</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.02292 – 0.33340</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">1.70782</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.09216521</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mdiploid 0.004</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.03311</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">-0.11344 – 0.17965</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.44278</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.65931134</td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">C Mhaploid 0.001</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.20429</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">0.12264 – 0.28594</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; ">4.90375</td> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center; "><strong>0.00000601</strong></td> </tr> <tr> <td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">my\)diff 0 | 0.29644 | 0.10951 – 0.48337 | 3.10815 | 0.00273414 | |||
|---|---|---|---|---|---|---|---|---|
| Observations | 76 | |||||||
| R2 / R2 adjusted | 0.479 / 0.434 | |||||||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$mmaxdiff
myr <- my$rmaxdiff_rt0
myc <- my$treatment
mytitle <- NULL
myylab <- "M-MAX-DIFF"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(3, 6), y = c(0.525), label = "***", size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_25.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)
Analysis: Assess effects of evolutionary treatment on total change in barcode abundance (summed across six intervals)
my <- myevo[order(myevo$bctID), ] # placeholder dataframe
# model & diagnostic plots
# ---------------------------------------------------- with initial
# proportions included in the model: mymod <- glm(my$tcc ~ my$treatment +
# my$cctcc_t0 + my$diff0, weights = my$rtcc_rt0); summary(mymod) # drop c as
# least significant term mymod <- glm(my$tcc ~ my$treatment + my$diff0,
# weights = my$rtcc_rt0); summary(mymod) # diff 0 still not
# significant...run the model set without it.
# without: mymod <- glm(my$tcc ~ my$treatment + my$cctcc, weights =
# my$rtcc); summary(mymod) # drop c as least significant term
mymod <- lm(my$tcc ~ my$treatment, weights = my$rtcc)
summary(mymod) # FINAL MODEL!
##
## Call:
## lm(formula = my$tcc ~ my$treatment, weights = my$rtcc)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -5.7198 -1.3007 -0.0058 1.0657 5.4905
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.0219194 0.0027409 7.997
## my$treatmentCM+Ethanoldiploid0.001 -0.0072401 0.0032633 -2.219
## my$treatmentCM+Saltdiploid0.001 0.0046752 0.0035042 1.334
## my$treatmentCMdiploid0.00025 0.0182730 0.0056216 3.251
## my$treatmentCMdiploid0.004 -0.0007325 0.0043115 -0.170
## my$treatmentCMhaploid0.001 -0.0066256 0.0033076 -2.003
## Pr(>|t|)
## (Intercept) 0.0000000000185 ***
## my$treatmentCM+Ethanoldiploid0.001 0.02976 *
## my$treatmentCM+Saltdiploid0.001 0.18647
## my$treatmentCMdiploid0.00025 0.00177 **
## my$treatmentCMdiploid0.004 0.86559
## my$treatmentCMhaploid0.001 0.04904 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.15 on 70 degrees of freedom
## Multiple R-squared: 0.3746, Adjusted R-squared: 0.33
## F-statistic: 8.387 on 5 and 70 DF, p-value: 0.000002951
anova(mymod) # test term significance with an lm
hist(residuals(mymod), breaks = 25)
hist(expand_residuals(mymod, my$rtcc), breaks = 25) # this more accurately represents the distribution of residuals in the model output. -- tails look fine here.
plot(mymod) # check model fit
summary(mymod)$r.squared # for R2 value
## [1] 0.3746437
summary(mymod)$adj.r.squared # for adjusted R2 value
## [1] 0.3299754
# -----------------------------------------------------------------------------
# model output
# -----------------------------------------------------------------
tab_model(mymod, digits = 5, digits.p = 8, show.stat = T, wrap.labels = 100,
file = "000_Additional_File_26.html")
| Â | my$tcc | |||
|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p |
| (Intercept) | 0.02192 | 0.01655 – 0.02729 | 7.99719 | <0.001 |
| CM+Ethanoldiploid 0.001 | -0.00724 | -0.01364 – -0.00084 | -2.21861 | 0.02975813 |
| CM+Saltdiploid 0.001 | 0.00468 | -0.00219 – 0.01154 | 1.33418 | 0.18646947 |
| C Mdiploid 0.00025 | 0.01827 | 0.00725 – 0.02929 | 3.25053 | 0.00177244 |
| C Mdiploid 0.004 | -0.00073 | -0.00918 – 0.00772 | -0.16989 | 0.86559011 |
| C Mhaploid 0.001 | -0.00663 | -0.01311 – -0.00014 | -2.00311 | 0.04903884 |
| Observations | 76 | |||
| R2 / R2 adjusted | 0.375 / 0.330 | |||
# -----------------------------------------------------------------------------
# Visualize
# -------------------------------------------------------------------
myx <- my$treatment
myy <- my$tcc
myr <- my$rtcc
myc <- my$treatment
mytitle <- NULL
myylab <- "Total Change in Abundance"
myxlab <- "Treatment"
myoutline <- "grey20"
myfill <- "grey90"
myptalpha <- 0.35
mypalette <- setpalette
mytextsize <- 8
myheight <- 3.5
mywidth <- 7
myptsize <- ((((myr/max(myr)) * 10) * ((myheight * mywidth)/(7^2))) - min(((myr/max(myr)) *
10) * ((myheight * mywidth)/(7^2)))) + 1
p <- ggplot(my, aes(x = myx, y = myy, weight = myr, color = myc)) + geom_violin(color = myoutline,
fill = myfill, draw_quantiles = NULL, inherit.aes = F, aes(x = myx, y = myy),
trim = T) + geom_jitter(width = 0.15, height = 0, alpha = myptalpha, size = myptsize) +
geom_segment(x = 5.9, xend = 6.1, y = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), yend = weighted.mean(myy[myx == "CMhaploid0.001"],
myr[myx == "CMhaploid0.001"]), color = myoutline) + geom_segment(x = 4.9,
xend = 5.1, y = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
yend = weighted.mean(myy[myx == "CMdiploid0.004"], myr[myx == "CMdiploid0.004"]),
color = myoutline) + geom_segment(x = 3.9, xend = 4.1, y = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), yend = weighted.mean(myy[myx ==
"CMdiploid0.00025"], myr[myx == "CMdiploid0.00025"]), color = myoutline) +
geom_segment(x = 2.9, xend = 3.1, y = weighted.mean(myy[myx == "CM+Saltdiploid0.001"],
myr[myx == "CM+Saltdiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Saltdiploid0.001"], myr[myx == "CM+Saltdiploid0.001"]), color = myoutline) +
geom_segment(x = 1.9, xend = 2.1, y = weighted.mean(myy[myx == "CM+Ethanoldiploid0.001"],
myr[myx == "CM+Ethanoldiploid0.001"]), yend = weighted.mean(myy[myx ==
"CM+Ethanoldiploid0.001"], myr[myx == "CM+Ethanoldiploid0.001"]), color = myoutline) +
geom_segment(x = 0.9, xend = 1.1, y = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), yend = weighted.mean(myy[myx == "CMdiploid0.001"],
myr[myx == "CMdiploid0.001"]), color = myoutline) + geom_rug(sides = "b",
position = position_nudge(x = -0.75), alpha = myptalpha) + scale_color_manual(values = mypalette) +
geom_hline(yintercept = 0, lty = "dotted") + coord_flip() + theme_classic() +
theme(legend.position = "none") + ggtitle(mytitle) + ylab(myylab) + xlab(myxlab) +
scale_x_discrete(labels = c("CM \n Diploid \n 0.001", "CM+EtOH \n Diploid \n 0.001",
"CM+NaCl \n Diploid \n 0.001", "CM \n Diploid \n 0.00025", "CM \n Diploid \n 0.004",
"CM \n Haploid \n 0.001")) + theme(axis.title.y = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.title.x = element_text(size = 8,
face = "bold", margin = margin(0, 0, 0, 0, "pt"))) + theme(axis.text.y = element_text(size = 8,
angle = 0)) + theme(axis.text.x = element_text(size = 8, angle = 0)) + annotate("text",
x = c(2, 4, 6), y = c(0.0275, 0.06825, 0.026), label = c("*", "**", "*"),
size = 8, fontface = "bold")
p
# -----------------------------------------------------------------------------
# figure output
# ---------------------------------------------------------------
pdf(file = "000_Additional_File_27.pdf", width = mywidth, height = myheight)
p
dev.off()
## quartz_off_screen
## 2
# -----------------------------------------------------------------------------
rm(my, mymod, p, myc, myfill, myheight, myoutline, mypalette, myptalpha, myptsize,
myr, mytextsize, mytitle, mywidth, myx, myxlab, myy, myylab)